Refining the representations of high-latitude surface-atmosphere radiative coupling in the E3SM

Lead PI: Prof. Xianglei Huang, University of Michigan, Dept. of Climate and Space Sciences and Engineering, 2455 Hayward Street, Ann Arbor, MI 48109-2143. Tel: (734) 936-0491. Email: xianglei@umich.eduCo-I: Prof. Mark Flanner, University of Michigan, Dept. of Climate and Space Sciences and Engineering, 2455 Hayward Street, Ann Arbor, MI 48109-2143Co-I: Prof. Ping Yang, Texas A&M University, Department of Atmospheric Sciences, MS 3150, College Station, Texas 77843Co-I: Prof. Charles Zender, University of California, Irvine, Department of Earth System Science and of Computer Science, Irvine, CA 92697-3100Unfunded DoE Collaborator: Dr. Wuyin Lin, Brookhaven National Laboratory, Environmental and Climate Sciences Department, 75 Rutherford Dr., Upton, NY 11973

Administrative Point of Contact: Daniela Marchelletta, Project Representative, Office of Research and Sponsored Projects (ORSP), University of Michigan, Ann Arbor, MI 48109. Tel: (734) 936-1296. Email: dcmarch@umich.eduFunding Opportunity FOA Number: DE-FOA-0002230DOE/Office of Science Program Office: Office of Biological and Environmental Research DOE/Office of Science Program Office Technical Contact: Dr. Sally McFarlane/Dr. Xujing Davis (ESM)PAMS Pre-application tracking number: PRE-0000022481Topic areas as identified in Section I of this FOA: ESM model development (ESMD), specifically, to refine representations of high-latitude surface-atmosphere radiative coupling across the atmosphere, sea ice, land ice components in the E3SM for a better simulation of polar climates.

Budget summary for all collaborating institutes Collaborative Application Information

Name Institution

Year 1 budget

Year 2 budget

Year 3 budget

Total budget

Lead PI Xianglei

Huang

University of

Michiga

141,421 125,981 129,528 396,930

I

nCo-IMark

Flanner

CoI/Institute

PI

Ping Yang

Texas A&M

59,999 65,000 69999194,998

CoI/Institute

PI

Charles Zender

UC Irvine 67796 63,230 64,726 195,752

Collaborator

Wuyin Lin BNL 0 0 0 0

Total Budget 269,216 254,211 264,253 787,680

Table of Contents (need to be updated)I. Project objectives, potential impacts, relevance to the FOA ……..……..……..…………… 1II. Background, motivations, and relevant previous studies …..……..……..…………………… 2 II.1 The coupling of non-blackbody surface and longwave cloud scattering and potential impacts on the high-latitude regions ……………………………………….…..……..……………... 3II.2. Inclusion of realistic surface spectral emissivity into the CESM and its impact on simulated polar climate …………………………………………………………………………………………... 4II.3. Impact of longwave cloud scattering on radiation budget…………..…..……..……..……... 7II.4. The impact on simulated climate when both surface spectral emissivity and LW cloud scattering are taken into account ……………………………………….……..……..………………. 8II.5. The effect of 3-D radiative transfer and feasibility of including such effect in the LW radiation scheme …..……..……..……..……..……..……..……..……..……..……..……..……..… 9II.6. Spectrally consistent treatment for surface-atmosphere radiative coupling: implications for the sea-ice LW radiative transfer ………..………..……..……..……..……..……..…….…..…..… 11III. Proposed research………………………………………………………………………..……… 13III1. Subtask 1: Implement what we have developed with the CESM into the E3SM……………14III2. Subtask 2: Develop a sea-ice LW radiation code with consistent spectral bands as the RRTMG_LW and implement it into the E3SM ………………………………………………………14

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III3. Subtask 3: Assess the impact of both subtask 1 and subtask 2 on E3SM simulation .……. 16III4. Subtask4: Understand and update ECRAD code for E3SM implementation .……..…….. 17III5. Subtask5: Integrating ECRAD into the E3SM and testing in single column and full model modes…………………………..…………………………………………………..………… 18IV. Qualification, management plan, and timelines …………………………….. ……….….…… 19 Appendix: Progress from DOE-funded research …………………………………………..………. 21Appendix1: Biographical sketch …………………………………………………………….…..…… 23Appendix2: Current and pending support……………………………………… ……….…..…… 35Appendix3: References……………………………………………………………….. …….…..….. 46Appendix4: Facilities & other resources ………………………………….. …………………..….. 49Appendix5: Equipment……………………..……………………………………….…………..….. 50 Appendix6: Data Management and Sharing Plans………………………………………………… 50Appendix 7: Glossary of acronyms ………………………………………………………….…….. 51

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I. Project continuity, objectives, potential impacts, and relevance to the FOAThe proposed studies here are built upon two consecutive projects (one ESM-SciDAC from and one ESMD) led by the PI Huang at the University of Michigan. By these two projects, we have developed a global surface spectral emissivity database for earth system models (Huang et al., 2016), assessed the impact of such surface spectral emissivity on the simulated climate (Huang et al., 2018; Chen et al., 2019), studied the impact of cloud LW scattering on radiation budget (Kuo et al., 2017), surveyed different treatment of cloud LW scattering and identified a scheme with optimized performance and accuracy for the use in earth system model (Kuo et al., 2020), pointed out the necessity of including cloud LW scattering in high-latitude climate simulation (Chen et al., under review at PNAS), and incorporated surface spectral emissivity into the sea-icecryosphere component ofs in E3SMthe earth system model (Wolff and Zender, submitted to JGR). Moreover, we have incorporated these such schemes into the E3SM v1 and v2 (alpha version) and made thesesuch code brancheshing available to the E3SM community. The single most important conclusion from the two projects is that, unlike the tropics and mid-latitude, the longwave radiative coupling between surface and atmosphere is a necessity, not an option, for polar climate simulation.

Based on our findings summarized above, the proposed studies here focuses on refining the high-latitude radiative coupling between atmosphere and cryosphere components for future E3SM model. The overarching goals are to more accurately represent surface-atmosphere radiative coupling in both the atmosphere and surface components and, thus, to reduce systematic structural uncertainties in the radiative transfer calculation through the fully-coupled E3SM. Such reduction of structural uncertainty in radiative transfer across multiple model components can improve the fidelity of the model simulation and help expose the root causes of biases in the simulated high-latitude climates. Meanwhile, we also aim at adopting the software practices and workflows to make our modifications of E3SM most suitable for future exascale computing implementation. Specific objectives are: 1. Incorporate our treatments of surface spectral emissivity and cloud longwave scattering into the RRTMGP, a radiation code optimized for GPU and being considered by E3SM developers. Evaluate its performance with respect to the treatments in the RRTMG. We will modify the radiation treatments in the cryosphere components (i.e. land snow/ice and sea ice models) to ensure the same spectral bands of RRTMGP being used. 2. Our current treatment uses prescribed surface emissivity over land surfacess for each calendar month. We will make it diagnosed each time step from the simulated surface types and compositions from land models using the predefined surface types and their surface spectral emissivities. This treatment will enable us to fully capture the radiative impact of land surface change and use, e.g., the melt of permafrost and the increase coverage of short shrubland in the Arctic. 3. We will modify the coupler to pass the longwave spectral flux at the native RRTMGP bandwidths through different E3SM components. This will ensure spectrally consistent treatments of the longwave flux across all the E3SM components.

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4. Conduct fully-coupled simulations with implementations in (1)-(3) to assess the impact on the simulated climate, with a special focus on the simulated high-latitude climate. 5. Continue our testing of ECRAD, a radiation scheme developed by the ECMWF to account for 3-D radiative transfer for the high-resolution climate modeling. Implement ECRAD into the E3SM and test it in both single column (SCM) model and full model modes. We will use the AWARE and M-PACE campaign as the primary SCM test cases first before test it with fully coupled E3SM. 6. We propose to incorporate snow algae radiative properties into the SNICAR-AD radiation scheme that represents snow and ice spectral albedo in the E3SM. This will enable the cryospheric components of E3SM to interface seamlessly with the treatment of ice algae-biogeochemistry schemes.Potential impacts: The proposed study will provide a suite of fundamental improvements on the representation of radiative transfer in both the atmosphere and ice/snow components of the E3SM by removing unrealistic assumptions about surface spectral emissivity and atmospheric LW scattering, and by incorporating radiative parameterization of snow algae (which can drastically change surface optical properties). By assessing the feasibility of taking 3-D radiative transfer of clouds into account, it will make the E3SM atmospheric radiation scheme prepared for the high-spatial-resolution simulation. By reducing structural uncertainties in the LW radiation schemes in the E3SM, especially the LW coupling between atmosphere and surface, it can also help better expose parametric uncertainties in the model tuning and thus facilitate the diagnosis of the identified biases in the E3SM simulation. The proposal team consists of internationally renowned experts in the atmosphere and cryosphere radiative transfers and core developers for the E3SM. The team members complements each other in terms of expertise and they have productive collaborations before. The proposed improvements include schemes that have been developed by the team and are immediately implementable (i.e. surface spectral emissivity and cloud LW scattering scheme), as well as schemes that need further work and evaluation (i.e. ECRAD scheme for 3-D cloud radiative transfer and the treatment of snow algae radiative properties). By such a sequence of model improvement “in pipeline”, we expect that the team can continuously improve the radiation schemes, build a cohesive strength in radiative transfer for high-resolution earth system modeling, and contribute to the overall success of the E3SM.Relevance to the FOA: The proposed studies are for the ESM model improvements, particularly for improving the surface-atmosphere radiative coupling in the high-latitudes, as well as for making the relevant code components and the coupler suitable for such improvements and for future exascale computing. By its foreseeable impacts on simulated high-latitude surface climate, the proposed study is also relevantated to an ESM Science Research Question, namely, the ocean-atmosphere-cryosphere interactions in both polar regions. II. Background, motivations, and relevant previous studiesRadiation schemes in current atmospheric general circulation models (AGCMs) are relatively mature compared to other schemes such as the cumulus scheme and aerosol-cloud microphysics scheme. Radiation schemes have been developed with a universal benchmark and “ground truth” reference, i.e., the line-by-line radiative transfer calculation. As a result, clear-sky radiation schemes are all well calibrated against the line-by-line benchmark and are usually deemed to be the

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most robust parameterizations in the AGCM (Held, 2005). While water vapor continuum remains an issue to be improved, it is an issue more about physical understanding rather than about parameterization. Therefore, as far as the representation of radiative process in the earth system models (ESMs) is concerned, major improvements come from the representation of radiative properties of clouds, aerosols, and different surface types (e.g., snow algae), as well as the coupling between atmosphere and surface components (land, ocean, sea ice, etc.). Meanwhile, accurate radiative transfer calculation, especially when scattering is considered, demands tremendous amount computing resources. How to make optimal compromise between accuracy and computing cost of the radiation scheme is an issue that always needs to be considered for ESM developments. As a result, certain approximations must be adopted to make the radiation scheme computationally affordable. However, in the process of justifying such approximations, traditional radiation scheme developers have paid much more attention to the tropics and mid-latitudes than to the high-latitudes. Certain approximation adopted in mainstream radiation schemes can be entirely justifiable for the tropics and mid-latitude but indeed not appropriate for the high-latitude. A perfect example is that cloud longwave scattering can be ignored in the tropics and mid-latitude but not in the polar regions (Chen et al., under review with PNAS). High-latitude climate and its changes has caused a lot of recent attentions, partly because the direct socioeconomical impact of polar amplification (e.g. an ice-free Arctic summer impacting energy and shipping sectors). To improve the simulated high-latitude climate and its changes by the E3SM thus is a meaningful question with practical applications. High climate is featured with a tight coupling between surface and atmosphere. Unlike the coupling in the extra polar regions, surface cryosphere processes play an important and even critical role in such coupling in the high-latitudes. As far as radiative coupling between high-latitude surface and atmosphere is concerned, following are outstanding issues in our opinion:(1) Uncertainty in representing the surface radiative properties, e.g., LW spectral emissivity and SW spectral albedo of different types of surfaces.(2) Consistency in the coupling of radiative flux among different modules in the fully coupled model. For example, in the E3SM as well as many other mainstream GCMs, the surface is assumed to be a blackbody in their atmospheric module but a graybody in the surface modules. (3) Approximations about cloud radiative transfer, such as neglecting LW cloud scattering and assumptions of plane-parallel clouds (i.e. no horizontal radiative flux from cloudy to clear-sky subgrids). The plane-parallel approximation can be particularly in question when the model spatial resolution becomes higher and higher. The LW cloud scattering, as our current work pointed out, is indeed an indispensable physical process for high-latitude climate. All three issues mentioned above are structural uncertainties. We propose to address these structural uncertainties and associated issues in the radiation schemes across different model components in the E3SM. Our rationale is that we have sufficient physical understandings, a suite of observations, and computational resources to significantly reduce such structural uncertainties in the current treatments of radiation in the E3SM (both atmosphere and surface models). By doing so, we can improve the fidelity of simulated high-latitude radiative transfer in the coupled system, and thus reduce simulation biases

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due to such structural uncertainties. In the following subsections, we will articulate in detail the background and motivations for our proposed studies.II.1. Seasonally dependent impact of cloud longwave scattering on the polar climate: why surface-atmosphere coupling matters

Accurately modeling radiative transfer in a scattering atmosphere is computationally infeasible for climate model simulations. As a result, reasonable approximations and simplifications have to be made to make computational cost affordable. One such approximation widely used in current climate models is the neglect of cloud LW scattering. Chen et al. (2014) and Kuo et al. (2020) surveyed climate models that participated in the IPCC 5th Assessment Report and found that only models from three modeling centers considered LW scattering (Supplementary Information). The traditional justification for this approximation is twofold: (1) the imaginary parts of the index of refraction of both ice and liquid in the LW are orders of magnitude larger than the counterparts in the shortwave. Therefore, the overall attenuation of longwave radiation by clouds is largely caused by absorption rather than scattering. (2) Furthermore, the LW has strong gaseous absorption by H2O, CO2, O3 and other trace gases. The single-scattering albedo of a vertical layer in the atmosphere, which describes the probability that an attenuated photon is scattered instead of being absorbed in the layer, can be expressed as

ω=ωc τ c

τair+τ c (1),

where τ air and τ c are the extinction optical thickness of gases and cloud in the layer, respectively, and c is the single-scattering albedo of cloud. Note that all variables in Eq. (1) are frequency dependent. As long as τ air≫ τc , the scattering in the layer is negligible regardless of the value of c. Such conditions usually valid for the spectral bands with strong greenhouse gas absorption, e.g., the center of the 15m CO2 band (630-690 cm-1) with τ air ranging from 100 to ~9000 in the troposphere. This is also the case for the H2O bands as long as atmospheric water vapor is abundant, basically outside polar regions.

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Figure 1. (Adopted from Chen et al., under review with PNAS). (a). The imaginary part of the index of refraction for ice (solid line) and liquid water (dotted line) in the longwave (LW). (b) The single scattering albedo of ice particles and water droplets as a function of

frequency in the longwave. The effective diameters of ice particles are 20 m (solid line) and 60m (dashed line), respectively. The effective diameters of water droplets are assumed to be 20 m. (c). A 10-year (2006-2015) zonal-mean climatology of total column water vapor derived from the NASA MERRA-2 reanalysis. TCWV averages for JJA, DJF, and the annual mean are plotted in orange, blue, and red, respectively. (d). Two plots are both based on spectral flux derived from collocated CERES and AIRS observations (Huang et al., 2014). Left panel: Percentage contribution of 350-630cm-1 flux to the entire outgoing longwave radiation (OLR) as a function of latitude in the same 2006-2015 period. Right panel: Percentage contribution of zonal-mean 350-630cm-

1 flux to OLR in each calendar month derived from the same 10 years of collocated AIRS and CERES observations.

While the above reasonings are generally applicable to water vapor bands in the tropics and mid-latitudes, they break down in polar regions for three reasons. First, the imaginary part of the index of refraction of ice has a local minimum around 400 cm-1; as a result, ice clouds can have a single-scattering albedo as large as 0.6-0.8 over 350-630 cm-1, a portion of the far-IR spectrum (Fig. 1a & 1b). Second, the same portion of the far-IR spectrum, where ice clouds exhibit strong scattering, contributes ~35-40% of outgoing longwave radiation in the polar regions (Fig. 1d); thus its contribution to energy budgets is not small. Third, the high-latitude TCWV is much smaller than the TCWV in extra-polar regions. Owing to its high elevation, the TCWV in the deep Antarctic is often <2mm, and the variation from summer to winter is a factor of ~2.5. In the Arctic, the TCWV is slightly above 1cm only in the summer and the summer-to-winter variation can be a factor of 4~5. (Fig. 1c). Since the optical depth is proportional to the density of the absorber, the optical depth of water vapor in high latitudes is also significantly less than in the extra-polar regions. Therefore, even for the same ice cloud, the scattering effect for the entire atmospheric layer can be larger in the high latitudes than in the rest of the globe simply because τ air caused by water vapor absorption is small in the polar regions. Such scattering effects can be especially important over the aforementioned far-IR region where ice scattering effects peak and contribute to a large portion of the LW radiation budget.

Because atmospheric sub-grid physical processes must be parameterized, which processes to include and which parameterization schemes to use are usually assessed using atmospheric model simulations forced by observed SST and sea ice (prescribed SST run). Then the LW radiative boundary condition is prescribed over oceans, and the surface is not allowed to respond to changes in downward LW flux.

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This raises an issue in assessing cloud LW scattering effects. Compared to a purely absorptive cloud, a scattering cloud can reflect upwelling LW radiation back to the surface. Such a change of downward LW flux can alter the surface energy budget and the surface temperature needs to change. However, this chain of response cannot be represented in such prescribed SST runs.

To investigate the ice cloud LW scattering effect on the simulated climate, we modified Community Earth System Model (CESM) version 1.1.1 to include ice cloud LW scattering treatments (Chen et al, under review with PNAS). The scattering effects of liquid clouds are generally small over the entire LW (Fig. 1b) and are not included in this study. Three sets of experiments were carried out. The first set of experiments enables the ice cloud LW scattering effect (hereby referred to as “Scat”). The second set is the same, except cloud longwave scattering is turned off by setting the cloud extinction optical depth to the absorption optical depth and the cloud single scattering albedo to zero (“noScat”). To examine the contributions from the far-IR bands, the third set is also the same, except the ice cloud scattering in the far-IR bands is turned off (“noFIR”). The impact of ice cloud LW scattering on the simulated climate is then deduced by differencing the results from Scat and noScat runs, or from noFIR and noScat runs. To understand how ice cloud LW scattering interacts with surface energy processes, these three experiments were carried out with a slab-ocean model (SOM), where SST and sea ice can have thermodynamic responses to the change of surface energy. We also carried out the same three sets of experiments with fixed climatological SST. Four 30-year ensemble runs were analyzed in each experiment to account for model internal variability.

Fig. 2 shows the impact of including ice cloud LW scattering on the simulated zonal-mean SAT climatology for December-January-February (DJF) and June-July-August (JJA) periods, respectively. For prescribed SST runs, including or excluding LW scattering has little impact on simulated SAT except in the Antarctic region. This is consistent with traditional wisdom that LW scattering matters little in prescribed SST simulations. The simulated SAT difference (SAT) south of 60oS is explainable because the Antarctic continent covers a majority of the region and the land surface temperature can respond to the changes due to the ice cloud LW scattering. Over the Arctic, the ensemble-mean SAT is nearly zero but the SAT of an individual member can be either positive or negative. This indicates that, when SST and sea ice are prescribed, including or excluding LW scattering behaves as a noisy perturbation on top of the already large internal variability of Arctic climate, and the ensemble-mean difference in SAT is not statistically different from zero.

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Figure 2. (Adopted from Chen et al, under review with PNAS) Left panel: Changes of boreal winter (December to February, DJF) zonal-mean surface air temperature (SAT) due to

inclusion of ice cloud LW scattering in the CESM. Each solid line shows an ensemble-mean difference and shading shows the spread of four 30-year ensemble runs. Red indicates the difference between including scattering (Scat) and not including scattering (noScat) in the

slab-ocean simulations (SOM). Blue shows the same difference from the prescribed-SST simulations. Yellow is the same as red except that far-IR ice cloud scattering is turned off.

Right panel: Same as left panel except for austral winter (June to August, JJA).

However, the zonal-mean SAT difference due to ice cloud LW scattering for the SOM runs is positive everywhere in all ensemble members (Fig. 2). Ensemble spreads in SOM runs are well separated from their counterparts in the prescribed SST runs. While ensemble-mean SAT in the tropics and mid-latitudes is ~0.5K for both DJF and JJA periods, it is ~1.8K in the Arctic winter, 3-5 times larger than in the Arctic summer. Moreover, all ensemble members show consistently positive SAT increases in the Arctic in spite of the large internal variability. Two factors explain the large contrast between the Arctic summer and winter SAT: (1) the seasonality of Arctic TCWV as mentioned above (less winter TCWV implies a stronger LW scattering effect); and (2) the absence of shortwave radiation and reduced surface turbulent heat flux due to extensive ice coverage in winter lead to a radiative boundary layer with LW radiation playing a dominant role in regulating SAT (Serreze & Barry 2005; Overland et al., 1991). In contrast, solar radiation plays a lead role in the summer Arctic surface energy balance. Moreover, summer snow and ice melting consumes energy and reduces energy available to further warm the surface. Similar contrasts between winter and summer SAT can be seen in the Antarctic.

Yellow curves in Fig. 2 show the impact on zonal-mean SAT when ice cloud scattering is turned off in the far-IR but on in the mid-IR (i.e. noFarIR – noScat). Mid-IR scattering contributes to about half of total SAT. The mid-IR scattering primarily results from window regions, where gaseous absorption is weak and thusτ air is small. However, for both Arctic and Antarctic winter, SAT in the noFIR run is much smaller than in the Scat run, indicating the dominant contribution of far-IR scattering to SAT in the Scat run. Such a contrasting role of ice cloud far-IR

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scattering between polar winter and extra-polar regions can be largely understood using Eq. (1) and is primarily due to the drastic winter difference in TCWV (and hence ∆ τair) between extra-polar and polar regions. It is also partly due to the increasing importance of far-IR contributions to the LW radiation from tropics to polar regions (Fig. 1d).

Fig. 2 shows the importance of evaluating cloud LW scattering effect with the coupled model. without surface-atmosphere radiative coupling, enhanced LW absorption due to cloud LW scattering has a minor impact on the simulated climate, like what have been shown before. When coupling is allowed, its impact on the polar surface climate is amplified through a positive atmosphere-surface feedback, especially in winter, due to the seasonally varying role of LW radiation in determining surface temperature. The domain-averaged budget analysis further quantifies the importance of modeling atmosphere-surface LW coupling to correctly assess the role of cloud LW scattering. These indicate that cloud longwave scattering is an indispensable process for high-latitude climate simulation and thus should be included in all climate models.

Further diagnosis in Chen et al. (under review with PNAS) revealed that, without surface-atmosphere radiative coupling, enhanced LW absorption due to cloud LW scattering has a minor impact on the simulated climate, like what have been shown before in previous studies (Wu et al., 2019; Zhao et al., 2018; Jin et al. 2019). When surface-atmospherecoupling is allowed, its impact on the polar surface climate is amplified through a positive atmosphere-surface feedback, especially in winter, due to the seasonally varying role of LW radiation in determining surface temperature. The domain-averaged budget analysis further quantifies the importance of modeling atmosphere-surface LW coupling to correctly assess the role of cloud LW scattering. These indicate that cloud longwave scattering is an indispensable process for high-latitude climate simulation and thus should be included in all earth system models.II.2 Non-blackbody surface emissivity: another factor that warrants the surface-atmosphere radiative coupling

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Figure 3. Adapted from Fig. 1 in Huang et al. (2018). The spectral emissivities of water, ice, coarse snow, and desert. The emissivities are derived using appropriate radiative transfer techniques and the mid-IR portions of the emissivities have been validated against available measurements. Further details can be found in Huang et al. (2016).

While most ESMs assume spectrally independent surface emissivity and nonscattering clouds in their longwave radiation treatment, spectral variation of the surface emissivity indeed can be non-negligible, as shown in Fig. 1. Given the Kirchhoff’s Law, if a surface has spectral emissivity v at frequency v, its spectral reflectivity is (1-v). Therefore, as long as v is not unity, the surface is indeed reflective in the longwave and blackbody assumption adopted by overly dominant majority of ESMs is not applicable. Just like cloud LW scattering, this is a physical mechanism missing in current ESMs, including the E3SM.

Motivated by the study of Chen et al. (2014) and funded by the ESM-SciDAC program, the PI and his collaborators have developed a global surface spectral emissivity data set for the entire longwave spectrum (Huang et al., 2016), with specific aims for the use in the ESMs. Huang et al. (2018) incorporated the surface spectral emissivity treatments into the CESM version 1.1.1 and evaluated its impact on simulated climatology and climate changes. In this study, the surface spectral emissivity over land is prescribed for each calendar month according to Huang et al. (2016). Over sea ice and open oceans, the surface emissivity is diagnosed at each time step using the fractional coverage of ice and water within each grid. By ensuring the consistency of the broadband longwave flux across different modules of the CESM, the TOA energy balance in the simulation can be attained without additional tuning of the model. As for the global mean surface energy budget, inclusion of surface spectral emissivity leads to a decrease of net upward longwave flux at surface by ~1 Wm -2 and a comparable increase of latent heat flux. The most noticeable differences between the modified and standard CESM can be found in the high-latitude zones, especially for the sea ice coverage and surface air temperature.

The data set developed by Huang et al. (2016) can be used in two ways. It can be used to prescribe the spectral emissivity of each surface grid in a GCM for each calendar month, as used in Chen et al. (2019). Or the spectral emissivities of 11 different surface types can be used to compute the grid-averaged surface spectral emissivity at each time step for each surface grid, i.e., making surface spectral emissivity a diagnostic variable in the model simulation and a function of surface type at each time step. In our previous studies, including the studies with E3SM v1 and v2 alpha, we adopted a hybrid approach: the surface spectral emissivity over land is prescribed for each calendar month; over ocean and sea ice it is diagnosed from the fractional coverage of different surface types

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at each time step. This approach allows to consider the surface spectral emissivity change with respect to sea ice change but not land ice change. To fully represent the surface-atmosphere radiative coupling in the high latitudes, the longwave impact of the land ice change and permafrost melts needs to be considered. In another word, the surface emissivity over land should be computed from the surface type composition as well. This is one focus in our proposed study for further refining the surface-atmosphere radiative coupling in the high latitudes. II.3. Impact of longwave cloud scattering and surface spectral emissivity on the simulated Arctic surface climatology by E3SM v1

Figure 4. Upper left: Red line is the simulated surface air temperature (SAT) monthly-mean climatology over the

Arctic (60o-90oN) by the E3SM v1 fully coupled simulation (E3SM DECKv1b). The result by modified E3SM v1 with treatments of surface emissivity and ice cloud LW scattering is shown in blue. 15 years of simulations are analyzed here with 2000s forcing set. For comparison, the results from ERA5 15-year average is shown in black. Upper right: the differences between model results and ERA5 reanalysis shown in the upper left panel. Green horizontal dash line is the zero line. Lower panels: same as the upper panels except for the sea ice fraction averaged over the Arctic.

Motivated by the above two studies described in subsections of II.1. and II.2, we have incorporated the treatments of surface spectral emissivity and cloud LW scattering into the E3SM v1 and the main findings have been presented as a plenary talk to the community at the last E3SM all-hands meeting. The E3SM v1 has a prominent warm bias especially over the Arctic. In the wintertime the warm bias in surface air temperature averaged over the Arctic can be as large as 4-6K (Figure 4b). After we incorporated both surface emissivity and ice cloud LW scattering treatments, the biases in both surface air temperature and sea ice coverage in the Arctic can be significantly reduced. Such reduction, especially the reduction in the wintertime, is consistent with the interpretation in subsection II.1 why cloud LW scattering matters more in the wintertime than in the summertime for the surface climate. Note these treatments that we incorporated are not merely a retune of parameters in the current existing schemes. Instead, they address missing physical processes that are important to the polar climate, as articulated in previous two subsections and shown in Huang et al. (2018) and Chen et al. (under review with PNAS). Though Figure 4 are only from 15 years of simulation, such promising results with E3SM v1 motivates us to further understand the

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impacts of such improved surface-atmosphere LW coupling in the polar region on E3SM simulations and incorporate them into the future E3SM models.II.4. Feasibility of including such 3-D effect in the high-resolution E3SM simulation

It has been recognized for a long time that accurate radiative transfer in the presence of clouds needs to take full three-dimensional radiative transfer effect into account. While most 3-D radiative transfer studies focus on shortwave, the effect of 3-D radiative transfer on LW has been studied by Heidinger and Cox (1996), Han (1999), Han and Elligson (1999), and Takara and Ellingson (2000). For example, Heidinger and Cox (1996) found that 3-D effects of cumulus cloud increase its LW CRE at surface by 30%. Takara and Ellingson (2000) showed that neglecting the LW cloud scattering and 3-D effects can result in up to 10 Wm-2 errors in the LW downward flux at the surface. Gounou and Hogan (2007) estimated that ignoring such 3-D effect leads to 10% error in both the TOA and surface LW CRE for aircraft contrails.

Figure 5. Hourly downward SW and LW flux at the surface as observed by the AWARE campaign and simulate by the ECRad with cloud inputs from AWARE

observations and temperature and humidity from ECMWF-interim reanalysis. The simulations from all McICA (organe dots), TripleClouds (yellow dots), and 3-D solver

SPARTACUS (purple dots) are all plotted.Current GCMs and ESMs, including the E3SM, employ an independent

column approximation (ICA), which treats vertical radiative fluxes within each subgrid column but not any horizontal transport of radiative fluxes between such columns. The accurate full 3-D radiative transfer calculation is time consuming and not practical for any GCM simulations. A two-part study (Schäfer et al., 2016; Hogan et al., 2016) presented a scheme to include 3-D radiative effect in a two-stream scheme suitable for GCM applications, which considers the radiative transfer through cloud sides. The radiatively effective cloud edge length is parameterized in the scheme and extra terms are added to the two-stream equations to represent lateral transport between clear and cloudy regions. The modified two-stream equations can then be solved in matrix formulation. The solver, termed as SPARTACUS, is then coupled to RRTMG_LW and RRTMG_SW, the correlated-k models for gas absorption that is also used in the E3SM. Hogan et al. (2016) confirmed the accuracy of such scheme by performing broadband radiative cooling rate comparisons with full 3-D radiative transfer calculation by the Monte Carlo model “MYSTIC” for a

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cumulus field. They also note the need to further understand the accuracy of SPARTACUS for the global cloud fields. Based on these studies, Hogan and Bozzo (2016) developed a new radiation scheme, named ECRad, for the Integrated Forecast System (IFS) at the ECMWF. ECRad is the first radiation scheme in the global weather and climate model that represents 3D radiative effect.

Led by co-I Prof. Ping Yang at TAMU, we have studied the ECRad scheme under our currently funded E3SM project. ECRAD provides four different solvers for cloudy radiative transfer, namely homogeneous (plane-parallel), McICA (same as the E3SM default), tripleclouds (clear-sky, optically thin and optically thick clouds; Shonk & Hogan, 2008), and SPARTACUS (the 3D two-stream solver). An example of off-line radiative transfer calculation of ECRad with input from AWARE campaign observations (Ref***) is shown in Fig. 5. In general, the agreements between ECRad results and AWARE measurements on the SW flux are better than those on the LW flux. The flux difference among three difference configurations in the ECRad is noticeable when the downward SW flux is small (i.e. cloudy sky). We also noticed that the SPARTACUS solver in ECRad 1.1.0 is not stable; it sometimes can result in unrealistic flux results, particularly in the LW. All of these warrant further investigation of the scheme and its applicability in ESMs.

Though ECRAD still needs to be fully evaluated for a variety of cloud scenarios, studies by Hogan and Bozzo (2018) and preliminary results in Fig. 5 indicate its promising future in the E3SM, especially for the high spatial-resolution E3SM simulations where 3-D effect can be much more prominent. We believe that it is a scientifically appealing idea to use it to assess the 3-D cloud radiative effect and to study the feasibility of integrating it into the ES3M as an alternative option for radiative transfer scheme. II.5 Spectrally consistent treatment for surface-atmosphere radiative coupling across all the components

Compared with radiation parameterizations used in the atmosphere module, LW radiative transfer treatments in the surface modules are crude. Using both the sea-ice and land models in E3SM and CESM as examples, the LW radiation calculation is done only for a single, broad spectral band for the entire LW. This broadband approach is in sharp contrast to RRTMG_LW, the LW radiation scheme used in the CAM and EAM, which performs radiative transfer calculation over 16 spectral bands.

Such disparity between surface and atmosphere radiation schemes in terms of spectral resolution leads to a problematic issue in radiative coupling. Downwelling radiative flux from the atmosphere serves as the upper boundary condition for radiative transfer calculations in the surface modules. Meanwhile, upwelling radiative flux at surface computed by the surface modules serves as the lower boundary condition for radiative transfer in the atmosphere module. As a result, the surface modules (land, ice/snow, and ocean) can only provide a LW broadband upward flux at the surface but RRTMG_LW needs upward flux partitioned into 16 spectral bands for its lower boundary condition. To circumvent this dilemma in radiative coupling, the RRTMG_LW module in the E3SM simply assumes the surface as a blackbody and computes a radiative skin temperature based on the upward broadband

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LW flux (Left panels in Fig. 6). This treatment has a fundamental flaw: even if the broadband flux is made to be identical in the surface and atmosphere models, the spectral band partitioning of the LW flux is not preserved. Given that atmospheric absorption is highly dependent on wavelength, an incorrect partitioning of broadband flux at the surface can propagate to radiative transfer throughout the atmospheric column. To eliminate this inconsistency in surface—atmosphere radiative coupling, the same spectral bands should be used in the atmosphere and surface modules. The UCI team led by Prof. Charlie Zender has already developed a shortwave (SW) radiation code (dubbed SNICAR-AD"two-stream DISORT" or SNICAR Adding-Doubling2SD) consistent with SNICAR (snow-only; Flanner et al, 2007) to replace the original CICE SW radiation module used for snow, ice, and ponds in the E3SM (Dang et al., 2019). Recently, Wolff & Zender (submitted to JGR) investigated the instantaneous LW radiation biases due to assuming blackbody and neglecting spectrally-resolved emissivities for three crysopheric media: ice, snow, and ponds, submitted to JGR). They found that assuming blackbody rather than spectrally-resolved emissivities causes significant longwave biases over all three media surfaces. The difference between fluxes over different surface types is greater than the seasonal cycle over the same surface type.

In the development of coupled GCMs and ESMs in the last two decades, great efforts have been invested to develop coupler interfacing among all the modules to ensure consistency and conservation of mass, momentum, and mechanical energy, which has significantly improved the fidelity of simulations (e.g. the “climate drift” problem plaguing the climate modeling community in 1990s was solved by such coupler development). We argue, based on findings described above as well as in Huang et al. (2018), that having consistent treatment in the radiative coupling across atmosphere and surface modules in the E3SM (or any earth system model) can also improve the fidelity of model simulations.

Figure 6. Left panel: Current coupling between atmosphere and sea-ice models in the E3SM. is the broadband emissivity, Ts is the surface temperature in the sea ice model, and is the Stefan-Boltzmann constant. Tskin is the radiative skin

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temperature inverted from broadband upward flux at the surface. Right panel: proposed coupling, resolved into the RRTMG_LW spectral bands, where subscript i denotes the i-th RRTMG_LW band, and BV(Ts) is the Planck function at frequency v for temperature Ts, vi is the bandwidth for the i-th RRTMG_LW band. II.6 Treatment of snow algae optical properties

In high latitudes, the algae normally thrive in freezing water or lie dormant across the continent’s snow and ice. With increases of temperature, however, algae can grow with pink and flower-like spores. A drastic example is the recent blood-red “watermelon snow” in Antarctic’s northern Peninsula (Fig. 7), due to algae full bloom in response to the record high temperatures in Antarctica. The spectral albedo of such algae (Fig. 7) explains well why it looks blood-red: the spectral albedos of algae from UV to green band are much smaller than the counterparts of pure snow, which can be as low as 0.4. As a result, a small presence of algae pigments can increase the absorption of SW flux (especially from UV to green light) by the snow, similar to the case of black carbon in the snow. A recent study by Cook et al. (2020) estimates that algae growth is responsible for 10-13% of the total runoff from the bare ice in the south-western section of Greenland Ice Sheet in the summer of 2017.

Snow algae growth have been considered in the E3SM biogeochemical component. From an ESM development point of view, it naturally to include the radiative properties of such algae pigments into the radiation scheme of cryosphere component, given the important change of surface spectral albedo due to such pigment as shown in Fig. 7. This motivates us to propose a subtask on exactly this topic.

Figure 7. Left: A photo posted onto the Facebook page of the Ministry of Education and Science of Ukraine on Feb 24, 2020. Vernadsky Research Base is located on the Galindez Island off the coast of Antarctica’s northern Peninsula. Right: Spectral albedo simulated with a newly developmental version of SNICAR by co-I Prof. Mark Flanner, which incorporates algae consisting of any combination of four light-absorbing pigments (indicated in the legend). The algae mass mixing ratio in snow is 1000 ppm, and the dry cell mass fraction of different pigments is indicated in the legend. III. Proposed research

Our proposed studies are motivated by the rationales and previous works described in Section II. Fig. 8 depicts the proposed research components with lead and, if applicable, participating institutes for each subtask. Details of each subtask are described as follows.

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Figure. 8 A flow-chart summary of the proposed research. The Lead and participating (if any) institutes for each task are also labeled. W.r.t. stands for “with respect to”, and esp. for “especially”.III.1. Subtask 1: Incorporate surface emissivity and ice cloud LW scattering into RRTMGP and perform on-line assessment with the E3SM (Lead: UM PI Huang; participant: TAMU )

This subtask is relatively straightforward. We will incorporate what we have developed, validated, and evaluated so far under the current E3SM development project into the RRTMGP, the new generation of RRTM developed for the use in parallel computing environment (Pincus et al., 2019). RRTMGP is being considered for the future E3SM. Currently there are E3SM v2 branch code using the RRTMGP. Although it has the same number of LW and SW bands as in the RRTMG, but the bandwidths of a few bands are different. For example, Band #1 in the RRTMG_LW covers 10-350cm-1 but in the RRTMGP_LW it is 10-250cm-1. We will recompute the surface spectral emissivity, ice cloud optical properties for the bandwidths defined in the RRTMGP and then modified the RRTMGP in the same way as we have done with RRTMG in the E3SM v1.

In addition, the number of g-points used in k-distribution calculation is nearly doubled from the RRTMG to RRTMGP (244 g-points in total for the SW and 256 for the LW). Fig. 4 in Pincus et al. (2019) compares the TOA and surface fluxes from RRTMG and RRTMGP when the input profiles are identical. While the results from RRTMGP are in more agreement with the line-by-line benchmark results, the OLR from RRTMGP tends to be smaller than the counterparts from the RRTMG and the downward LW flux at surface from RRTMGP tends to be larger than its RRTMG counterpart. As a test, we have interfaced the RRTMG and RRTMGP in the same E3SM v2 alpha model and ran the code for one time-step under each configuration. The TOA and surface LW flux differences, as shown in Fig. 9, are largely consistent with the off-line comparison in Pincus et al. (2019):

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the RRTMGP has a smaller OLR over majority of the globe but a larger surface downward LW flux. Such difference between the RRTMGP and RRTMG warrants further assessment of their impact on the simulate climate in the on-line E3SM simulation. We will carry out prescribed SST runs first to assess to what extent the RRTMGP-bases E3SM differs from the RRTMG-based ESM, in terms of radiation budget (TOA and surface), precipitation, and other key climate variables. We will carry out such assessment before and after our implementation of surface spectral emissivity and ice cloud LW scattering.

Figure 9. The longwave flux difference after one-time step when the E3SM v2 alpha version is interfaced with RRTMGP and RRTMG, respectively.

RRTMGP included a cloud LW scattering treatment based on Tang et al. (2018) while our cloud LW scattering is a hybrid 2-stream/4-stgream approach (Kuo et al., 2020). We will assess the two scattering schemes as well after the implementation. Based on the comparisons done in Kuo et al. (2020), likely the hybrid 2-sream/4-stream scheme has a better performance in terms of the computation cost and accuracy than the default LW scattering scheme in RRTMGP.

Code implementation consideration: The code implementation is straightforward as we have done similar work with the E3SM v1. We will make our implementation as options and keep the original schemes in all implementations. Extra consideration will be paid to maintaining the bit-for-bit consistency of the updated E3SM with the original E3SM when the new developments are switched off, through implementation design and testing. This consideration will be applied to all following proposed implementations into the E3SM code base. III.2 Subtask 2: Make land surface spectral emissivity diagnosed from the simulation at each time step (Lead: UCI, Zender; particiant: UM)

As explained in Section II, our current treatment only diagnoses the surface spectral emissivity over oceans and sea ices at each time step and prescribes the land surface spectral emissivity in each calendar month based on the Huang et al. (2016). A drawback of such treatment is that it cannot take the surface longwave flux changes due to land surface usage and changes into account. As explained in subsection II.1, this is generally not an issue for tropics and mid-latitude where the TCWV is large (so atmosphere is opaque over the water vapor bands). But it becomes more an issue in the high-latitude where TCWV is small. The surface spectral emissivity of permafrost is more like a mixture of bare ice and soil. After

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the permafrost melting, the surface spectral emissivity is more like that of short shrubland (Huang et al., 2016). The seasonal cycle of land ice and snow growth also leads to changes in surface spectral emissivity. We will modify the radiation component in the ELM (E3SM Land Model) to make the surface spectral emissivity diagnosed as a function of surface type composition at each time step, just like what we have done for the sea ice and ocean components. By such prognosing the entire surface LW spectral emissivity in polar regions, it allows for full feedbacks between atmosphere and polar surfaces.

Code implementation consideration: UCI team is well-poised to lead this subtask since we have just finished the analogous LW task in MPAS-Seaice, and have already harmonized the SW codes of ELM and MPAS-Seaice to use SNICAR-AD.. We will create a flexible infrastructure to re-bin optical properties of all land surfaces to finer resolutions, possibly the full RRTMG LW grid, although coarser approximations are likely in spectral regions with small gradients. Though our focus here is on high-latitude processes, the proposed code structure modifications will be applicable to the entire globe.III. 3 Subtask 3: Modify the coupler for passing spectral flux across different components and evaluate the overall simulation performance (Lead: UM, Huang; co-Lead: UCI; participant: Wuyin Lin of BNL)

Currently the coupler only passes the broadband surface upward SW and LW flux from surface components to EAM (E3SM Atmospheric Model), serving as the lower boundy conditions for the radiative transfer solver in the EAM. Our implementation in subtasks 1-2 will make the surface components all calculating spectral flux at the same bandwidth as RRTMGP (or RRTMG). Therefore, what should be passed through coupler should be the spectral fluxes instead of broadband flux. With closely working with

Code implementation consideration: Shifting from 1 band to the full 16-band RRTMG_LW grid would require passing at least 32 more fields (up- and down-welling spectral fluxes for each RRTMG LW band) through the coupler. Expanding the number of shortwave fields to the full 14-band RRTMG_SW grid would require passing a factor of (up to) seven more SW fields. These expansions will require close coordination with Coupler experts from the E3SM Software Engineering group. We will look for efficiencies that can be gained by aggregating bands while preserving the essential LW spectral features such as mid-IR atmospheric window (1080-1180cm-1), "dirty window" in the far IR bands (Chen et al., under review with PNAS).

After all the above modifications implemented into the E3SM, we will carry out a series of test runs to assess and understand the impact to the simulated climate. For comparison purpose, both the modified E3SM and the standard E3SM will be used so the simulation results can be contrast with each other, in addition to the comparisons with observations.

Modeling strategy and validation plan: To better understand the effect due to the modification of physics alone, we will first perform a series of short-term hindcast simulations to understand the impact of each modification. The understandings will primarily rely on the comparisons with observations and with the standard E3SM hindcast simulations. This will set the baseline for us to assess the performance of all changes in the fully-coupled run. We will then carry out multiple decades of simulations using current forcing scenario so the simulated climatology can be compared with observations. Table 1 summarizes key

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observations data sets that we plan to use in the analysis. Some of the data sets are already included in the standard E3SM diagnostic package. Some are available online and have been used by the UM team before (e.g. BSRN surface radiation budget data sets). One data set, the TOA band-by-band LW flux and CRE, was developed by the PI’s group at UM. It provides observed band-by-band decomposition of broadband OLR and LW CRE, making it particularly suitable for testing our modified E3SM since our modification is aimed at improving the spectral consistency in the simulation.

We expect iterations between this subtask and Subtasks 1 and 2 as the data-model evaluation might give us hints for fine tune of the modifications that we have done in Subtasks 1 and 2. The UM team will be primarily responsible for carrying out simulations and performing the data analysis. Variables Datasets References or Remarks

TOA broadband flux and CRE (LW and SW)

CERES EBAF and DoE ARMBE dataset

Loeb et al. (2009)

TOA band-by-band LW flux and CRE

The PI’s dataset from collocated AIRS and CERES observations

Huang et al. (2008); Huang et al. (2010); Chen et al. (2013), Huang et al. (2014).

Surface energy budget

GEWEX BSRN, CERES SARB datasets, and DoE ARMBE dataset

BSRN: baseline surface radiation network (station data sets)SARB: surface and atmospheric radiation budget (gridded data sets).

Cloud fraction ARMBE, ISCCP and MODIS

Satellite simulator, COSP, will be used for this evaluation (Bodas-Salcedo et al., 2011)

Cloud water content CloudSat retrievals of liquid water content and ice water content

Austin et al. (2009) and Deng et al. (2015)

Precipitation GPCP data sets, TRMM observations

Temperature and humidity

ERA-interim reanalysis, AIRS retrieved data

AIRS-derived quantities are available from the EarthGrid network websites

Wind, geopotential height

ERA-interim reanalysis &NCEP-DOE Reanalysis II

Table 1. Proposed datasets to be used in evaluations of model simulations.III. 4 Subtask 4: Further assess ECRrad code for E3SM implementation (Lead: Texas A&M, Yang; participant: UM)

We will continue our work with ECRad. We will replace ice cloud scheme in the ECRad with the ice cloud optics scheme adopted in Kuo et al. (2020), which is the same as what we used for our modifications to the RRTMG and RRTMGP. The ice cloud scheme is consistent with a state-of-the-art ice cloud optical property

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model used in satellite remote sensing (Platnick et al., 2017) based on advanced light scattering computational capabilities (Yang et al., 2013). As mentioned in subsection II.4, we have identified issues in the current ECRad and will do further testing to understand such issue. Then we will carry out the test of ECRAD in the single column model (SCM) configuration of the E3SM and compare with ARM observations. Such SCM simulations, with large-scale tendencies prescribed according to the observations, is particularly suitable to assess the impact of a new sub-grid parameterization on the simulated radiation fields and cloud fields (Zhang et al., 2016). The ARM observations to be used here will be from two high-latitude field campaigns, namely M-PACE in 2004 (ref***) and AWARE in 2016-2017 (ref***). We will focus on how good the SCM can simulate the surface radiation budget and cloud macroscopic properties, especially comparing to the default SCM without the ECRAD scheme and to the actual ARM observations. After rigorous tests with the SCM, we will evaluate the performance in the full E3SM. The design of simulations and validation plan are largely the same as what is described for the subtask 3. In addition, we will also assess its computational performance with respect to RRTMGP and the impact on overall computational cost of the fully-coupled simulation. III.5 Subtask 5: represent the radiative properties of snow algae in the E3SM surface radiation schemes (Lead: UM Mark Flanner; Participant: UCI)

We will also incorporate a framework for representing snow and ice algae and their associated impacts on albedo in the land surface (ELM) and land ice (MPAS-LI) components of E3SM. OurThe new SNICAR-AD model (Dang et al, 2019) has already been included will soon be included in the land, sea-ice, and land ice model components of E3SM v2, so that albedos of cryospheric surfaces are represented self-consistently across E3SM, including with the same spectral grids. We will modify SNICAR-AD to accommodate additional types of light-absorbing impurities, building on its current capacity of handling black carbon, dust, and organic carbon. We will initially add a single generic algae species to account for snow and ice darkening by algae. The spectral optical properties will be derived from in-situ measurements of chlorophyll and carotenoid pigment refractive indices (Dauchet et al., 2015; Cook et al., 2020), and we will initially assume typical representative ratios of these pigments. Our initial efforts will focus on the Greenland Ice Sheet, where algae has been shown to accelerate surface runoff in the southwest “dark zone” (e.g., Cook et al., 2020). We will start by prescribing spatially- and monthly-varying algal cell abundance distributions derived from Sentinel-3 remote sensing (Wang et al., 2018) over 2016 – 2018. We will then modify the algae pigment abundances and optical properties as needed to ensure that visible albedo simulated by SNICAR-AD in the Greenland algal zone agrees well with MODIS and VIIRS albedo products.

Although our deliverable for this project will consist only of the prescribed algae scheme described above, the framework we develop will facilitate the development of future prognostic algae schemes that leverage the biogeochemical and physical components of E3SM. For example, the delivery of limiting nitrogen, phosphorous, and iron nutrients to algae could occur through prognostic dust deposition in the model. When melt runoff occurs in algal zones, these limiting nutrients could then be passed to the ocean component of E3SM, potentially influencing phytoplankton blooms in regions like the North Atlantic that are important for the global carbon cycle. Prognostic algal growth would also depend

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critically on physical state variables like sunlight, temperature, liquid water presence, and slope. These future efforts will necessarily involve biogeochemistry experts and other E3SM collaborators.IV. Qualification, management plan, and timelines

The team members have extensive track record of successful collaborations. The team members have great synergy in scientific expertise and model development, especially for the radiative transfer throughout all modules in the E3SM. Science qualification: The lead-PI Prof. Huang is specialized in the LW radiative transfer as well as diagnosis analysis of climate modeling and satellite remote sensing. Over the last decade he has published articles using different methods and different observations to evaluate GCM simulations. He has worked with modelers from different GCM centers such as NOAA GFDL, NASA GEOS-5, Environment Canada CCCma. He has been acquainted with LW radiation schemes in the GCMs since his postdoctoral stay at NOAA GFDL. The Co-I Prof. Yang is a world-renowned expert in scattering theory and radiative transfer. The Co-I Prof. Flanner is a leading expert in modeling snow physics including canopy-snow melting process. He has been actively participating in the development of snow and ice modules in the CESM. The Co-I Prof. Zender is also a leading expert in the cryosphere radiative transfer and actively participating in the E3SM development, especially its cryosphere components. The unfunded collaborator Dr. Wuyin Lin is a core developer of the E3SM atmosphere model including single column model and actively participate in various simulation campaigns for the E3SM science missions. Model development qualification: The PI and his group have experience working with the CESM (Chen et al., 2017; Chen et al., 2019; Huang et al., 2018) and E3SM. Profs. Flanner and Zender have extensively participated in the development of CESM and its predecessor (CCSM). Moreover, Prof. Zender contributes ongoing development and support to core E3SM diagnostics and analysis software (including A-Prime, MPAS-Analysis, E3SM Diagnostics, and Workbench) that uses his netCDF Operators to compute, regrid, and split E3SM climatologies. Dr. Wuyin Lin is a core developer for the E3SM with extensive experience in implementing and integrating new developments for model physical parameterizations. Management plan: The lead PI will oversee the entire project and monitor the progress of the project. The leading role for each subtask has been identified in Section III, right after each subtask title. Each institute will have a postdoc working with the institute PI to carry out the majority of the work. At UM, the postdoc will be co-advised by Profs. Huang and Flanner. A bi-weekly telecon will be held on a regular basis to update the progress and discuss issues standing in the way. Free project tracking softwares such as Confluence or Trellow will be used to track the progress of each subtasks. In addition to peer-reviewed publications, the findings from this project will be reported on the E3SM regular telecons, the E3SM all-hands meeting, as well as major national conference such as AGU or AMS conferences.

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Timelines: Using the subtask IDs defined in Section III, the timelines of the proposed research are as follows. The concurrent scheduling of subtasks 1-3 and subtasks 4-5 is possible due to the independent nature of the work.***

Subtasks Period Lead Major ParticipantsSubtask1: Incorporate surface emissivity and

ice cloud LW scattering into RRTMGP and

perform on-line assessment with the

E3SM

Months 1-12 Prof. Huang (UM)

Postdocs in UM and TAMU, Prof Yang

(TAMU)

Subtask2: Make land surface spectral

emissivity diagnosed from the simulation at

each time step

Months 1-12 Prof. Zender (UCI)

A postdoc at UCI, Prof. Flanner (UM)

Subtask3. Modify the coupler for passing spectral flux across

different components and evaluate the overall simulation performance

Months 12-30

Prof. Huang (UM)

Dr. Lin, a postdoc in the UM; and all

the co-Is for discussing simulation output.

Milestones/Deliverables: (1) A E3SM branch code with all modifications incorporated available to the E3SM community; (2) Full assessment of the impact on simulated climate; (3) 1-2 publications to describe the work and the findings.

Subtask 4: Further assess ECRrad code for E3SM implementation

Months 6-24 Prof. Yang (TAMU)

A postdoc at TAMU; Prof. Huang

(UM)Subtask5: represent the radiative properties of

snow algae in the E3SM surface radiation

schemes

Months 18-36

Prof. Flanner (UM)

A postdoc in UM, Prof. Zender (UCI)

Milestones/Deliverables: (1) A modified ECRad within E3SM code infrastructure; (2) Full assessment of ECRad-based SCM simulation with

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Appendix: Progress from DOE-funded researchI. Lead PI: X.L. Huang; Co-Is: M. Flanner, P. Yang, C. ZenderThe lead PI Prof. Huang (UM), together with co-Is Flanner (UM) and Yang (TAMU) has been funded by BER ESM-SciDAC program (DE-SC0012969), entitled “Major improvements on the longwave radiative interactions between surface and clouds in the polar regions in atmospheric global circulation model”, and then succeeded by ESM program (DE-SC0019278), entitle “Incorporate More Realistic Surface-atmosphere Radiative Coupling in E3SM” (Zender is also a co-I on this project). The first project focused on the modification of CAM module (atmosphere model) in the CESM and the second project transferred the knowledge gained from the first project into the E3SM. We developed schemes to incorporate realistic surface spectral emissivity into the LW radiation scheme of CAM and E3SM. Separately we have also updated ice cloud optics based on the most recent ice cloud scattering calculation and modified RRTMG_LW to include scattering in the longwave. So far, we have published seven peer-reviewed articles from the two projects, and two manuscripts under review (one with PNAS and the other with JGR). Below is a summary of each publication and its major results, in reversed chronological order.1. Chen, Y.-H., X. L. Huang, P. Yang, C.-P. Kuo, X. H. Chen, Seasonally dependent impact of cloud longwave scattering on the polar climate, submitted to Proc. Natl. Acad. Sci.(under review). This study pointed out the necessity of including cloud longwave scattering for the simulation of polar climate and explained why it has been ignored before: only coupled simulation can correctly show the impact of longwave scattering and the prescribed SST simulation cannot assess it correctly.2. Wolff, Z., C. Zender, Improvements in Atmospheric Longwave Radiation Due to Realistic Representation of Cryospheric Surface Emissivity in Earth System Models, submitted to JGR-Atmospheres. This study investigated the LW flux biases over snow, bare ice, and melt ponds surfaces due to the assumed surface spectral emissivity in the model. 3. Chen, Y.-H., X.L. Huang, X. H. Chen, M. G. Flanner, The Effects of Surface Longwave Spectral Emissivity on Atmospheric Circulation and Convection over the Sahara and Sahel , J. Climate, 32(15), 4873-4890, doi.org/10.1175/JCLI-D-18-0615.1, 2019. This study demonstrated the impact of desert surface emissivity on regional climate and beyond. By only changing the surface spectral emissivity in Sahara and Sahel region, we showed its impact on large-scale precipitation and circulation.4. Flanner, M. G., X. L. Huang, X. H. Chen, G. Krinner, Climate response to negative greenhouse gas forcing in polar winter, Geophys. Res. Letts., 45, 1997–2004. doi.org/10.1002/2017GL076668, 2018.5. Huang, X. L., X. H. Chen, M. G. Flanner, P. Yang, D. Feldman, C. Kuo, Improved representation of surface spectral emissivity in a global climate model and its impact on simulated climate , J. Climate, 31(9), 3711-3727, doi:10.1175/JCLI-D-17-0125, 2018. This study is another major result from the funded project. It incorporated the global surface emissivity dataset that we have published into the CAM module of CESM and formally evaluated the simulated climatology and climate change with respect to their counterparts using the standard CESM. 6. Kuo, C.-P., P. Yang, X. L. Huang, D. Feldman, M. Flanner, C. Kuo, E. Mlawer, Impact of Multiple Scattering on Longwave Radiative Transfer Involving Clouds, Journal of Advances in Modeling Earth Systems, 9, doi.org/10.1002/2017MS001117, 2018. This study is another major result from the funded project. It performed an off-line radiation benchmark calculation to assess the impact of LW scattering on TOA and surface LW radiation budget, as well as on the LW cloud radiative effect. It showed that the contribution from LW scattering is not negligible, and the significance of such contribution varies with spectral bands as well as geographical regions.

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7. Chen, X. H., X. L. Huang, C. Y. Jiao, M. G. Flanner, T. Raeker, B. Palen, Running climate model on a commercial cloud computing environment: A case study using Community Earth System Model (CESM) on Amazon AWS, Computers & Geosciences, 98, 21-25, doi:10.1016/j.cageo.2016.09.14, 2017. This is a technical-note style publication in which we documented the feasibility of running the CESM model on commercially available cloud computing, Amazon AWS, and benchmarked its performance.8. Huang, X. L., X.H. Chen, D. K. Zhou, X. Liu, An observationally based global band-by-band surface emissivity dataset for climate and weather simulations, Journal of the Atmospheric Sciences, 73, 3541-3555, doi:10.1175/JAS-D-15-0355.1, 2016. This is a major result from the funded project, in which we developed and validated a global surface spectral emissivity data set for the entire longwave spectrum. To our knowledge, this is the first ever emissivity data set over the entire longwave spectrum.9. Cheng, H. Z., X.H. Chen, X. L. Huang, Quantification of the Errors Associated with the Representation of Surface Emissivity in the RRTMG_LW, Journal of Quantitative Spectroscopy and Radiative Transfer, 180, 167-176, doi:10.1016/j.qsrt.2016.05.004, 2016. This study quantified the errors in radiative transfer calculation by RRTMG_LW when surface spectral emissivity is included. This study showed that, compared to line-by-line radiative transfer calculation, RRTMG_LW calculation is satisfactory when surface spectral emissivity is included. Direct contribution to the E3SM development: We have modified E3SM v1 and v2 alpha versions with surface spectral emissivity and ice cloud LW scattering treatments. The modification is now officially in the E3SM repository of E3SM (https://github.com/E3SM-Project/E3SM/pull/3392). We have presented in the plenary session of the E3SM all-hand meeting in 2018 and 2019. We also regularly participated in the E3SM water cycle telecon and NGD Atmosphere telecon for presenting our progress. II. Co-I: Mark Flanner Prof. Flanner is the PI of an DOE ASR project entitled "Using ARM measurements to improve the simulated vertical distribution of Arctic aerosols in the Community Atmosphere Model".  Current efforts for this project are focusing on parametric changes to the CAM5 aerosol and cloud treatments in the Modal Aerosol Model (MAM) that affect the simulated vertical distribution of black carbon, especially in the Arctic region.  This work has led to presentations at the AGU Fall Meeting (Jiao and Flanner, 2015) and 2017 DOE ARM/ASR Joint User Facility PI Meeting.III. Co-I: Charles Zender (UC Irvine)Prof. Zender is/was PI on multipletwo previous DOE-funded projects related to the Accelerated Climate Model for Energy (ACME, now E3SM), DE-SC0012998. In the first project (2014-2017), called "Lightweight Climate Analysis Tools for ACME", UCI supported ACME (now ACME/E3SM) with both software and expertise in high performance scientific computing, primarily analysis of the large datasets. UCI developed three new netCDF Operator (NCO) tools: a regridder (ncremap), a climatology generator and combiner (ncclimo), and a timeseries splitter (also ncclimo), for ACME/E3SM and the broader community. These tools are now fully integrated in the ACME-Unified (including A-Prime, MPAS-Analysis, E3SM Diagnostics) and Workbench suites. UCI published four papers and presented about a dozen talks and posters based on this work. UCI’s infrastructure work for E3SM has been renewed twice since then, in a project called The follow-on, "High-Performance Analysis and Regridding Support for ACME/E3SM" is currently underway, sponsored by LLNL. These projects supported development of E3SM-required NCO capabilities such as vertical regridding, CMIP6 workflow parallelization, and fast weight offline regridding weight generation.

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Zender’s group has also contributed to and is leading a series of DOE-funded projects to improve surface cryospheric processes In the second projectsince 2016. In (2016-2018), the project called "Harmonize Snow Radiative Processes Across ACME", UCI developed SNICAR-AD and implemented it inmade EALM and MPAS-Seaice to give both models equivalent treatments of snow in E3SM v2. This supported one graduate student who earned her PhD and one postdoc. The Intellectual contributions include , thus far, threeone publications, one currently submitted manuscript, and two manuscripts in preparation, and about a dozen eight talks and posters. The LLNL funded project “Improving Greenland ice sheet surface melt in E3SM” is currently underway and will activate the bare-ice and pond radiative treatments in SNICAR-AD (and thus MPAS-Seaice) in ELM over ice sheet surfaces. This will improve bare-ice and pond albedo and thus ice-sheet energy budget and surface melt. The LANL-funded project “Improving Ice Sheet Surface Mass Balance in E3SM Through Improvements to the Physical Snowpack Model” is also ongoing. UCI has replaced the previous ELM thin-snowpack (5-layers, 1 meter deep) snowpack model with a vertically extensive 12-layer model that simulates the full firn layer down to depths near pore-closeoff. This facilitates robust treatment of ice-sheet surface mass balance (SMB), storage of liquid melt, and improved surface density (which improves albedo). This work supports one post-doc and has resulted in one manuscript in preparation and about four talks at national conferences and workshops. Finally, in the project “Incorporate more realistic surface-atmosphere radiative coupling in E3SM” led by U. Michigan, UCI has developed, as described in this proposal, a spectral longwave emissivity model and implemented it in MPAS-Seaice. This work improves both the LW radiation and the surface temperature thermodynamics of the sea ice. It supports one graduate student who has submitted one manuscript and given about four talks at national conferences and workshops.CICE treat have equivalent radiative treatments of snowpack, and to evaluate, implement, and maintain ongoing improvements to the snow treatment in both models. This work is currently underway and forms the basis for improving the sea-ice radiative transfer in our ESM proposal. To date, we have created and optimized a new radiation code, called Two-Stream Discrete Ordinate (2SD) that can seamlessly use the SNICAR radiative properties and spectral grid, while also handling the extra sea-ice demands of bare ice and ponds. 2SD is operational in a development branch of MPAS-Seaice and is undergoing final tuning. 2SD eliminates the excessive solar absorption at large solar zenith angles that is predicted by all other two-stream models to our knowledge (including CICE and SNICAR), for all sizes of snow crystals and all snowpack depths. This supported one graduate student who earned her PhD and one postdoc. Intellectual contributions include, thus far, one publication, one currently submitted manuscript, and two manuscripts in preparation, and about eight talks and posters.(Charlie ***, please add more progresses here if applicable)III. Collaborator: Wuyin Lin (BNL)Dr. Lin has been supported by Accelerated Climate Modeling for Energy (ACME) and E3SM Project. He has been a key contributor to the development of the E3SM v1. He has been an integrator for the E3SM atmosphere model, supporting implementation, review, testing and integration of new code developments. He led the effort of tuning the high-resolution ACME V1 model with extensive use of the Cloud Associated Parameterization Testbeds (CAPT) framework and delivered a well-tuned E3SM atmosphere model at high-resolution. He was a major contributor to the implementation, testing and evaluation of candidate convective parameterizations, leading to the adoption of the Cloud Layers Unified By Binormals (CLUBB) scheme as a unified parameterization for turbulence and shallow cumulus in the current E3SM model.

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Letter of Support for the participation of unfunded collaborator Dr. Wuyin Lin

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Xianglei Huang (http://www.umich.edu/~xianglei)Degrees and postdoctoral trainingUniversity of Science & Technology of China Atmospheric Physics B.S. 1997California Institute of Technology Planetary Science M.S. 2000, Ph.D. 2004Princeton University Postdoctoral Research Associate, 06/2004-06/2006

Research and Professional AppointmentsAssociate Professor, University of Michigan 09/2012-present

Visiting Research Scholar, Princeton University (Sabbatical leave) 09/2013-12/2013Assistant Professor, University of Michigan 09/2006-08/2012Associate Research Scholar, Princeton University 06/2006-08/2006

Research Interest and Specialty Being trained at Caltech and Princeton/GFDL, Prof. Huang has a balanced expertise on both satellite remote sensing and GCM modeling, with a focus on radiation and climate especially from the spectrally resolved perspective. Prof. Huang has been working on the evaluation of GCMs with satellite observations especially the spectral radiance measurements for more than one decade. He has published studies on evaluating UCLA AGCM 7.0, NCAR CAM2, GFDL AM2, NASA GEOS-5, and Canada CanAM4 models. He established algorithms to derive band-by-band fluxes over the entire longwave spectrum directly from the AIRS measurement. He has developed a global surface spectral emissivity dataset for modelers to use. In recent years, he has also worked closely with DoE E3SM developers for improving the longwave radiation schemes in the global climate models.

Ten most closely related publications (postdoc and graduate student advisees underlined). 10. Chen, Y.-H., X.L. Huang, X. H. Chen, M. G. Flanner, The Effects of Surface Longwave Spectral

Emissivity on Atmospheric Circulation and Convection over the Sahara and Sahel, Journal of Climate, 32(15), 4873-4890, doi.org/10.1175/JCLI-D-18-0615.1, 2019.

9. Huang, X.L., X. H. Chen, Q. Yue, Band-by-band contributions to the longwave cloud radiative feedbacks, Geophysical Research Letters, 46, 10.1029/2019GL083466, 2019.

8. Huang, X. L., X. H. Chen, M. G. Flanner, P. Yang, D. Feldman, C. Kuo, Improved representation of surface spectral emissivity in a global climate model and its impact on simulated climate, Journal of Climate, 31(9), 3711-3727, doi:10.1175/JCLI-D-17-0125, 2018.

7. Huang, X. L., X.H. Chen, D. K. Zhou, X. Liu, An observationally based global band-by-band surface emissivity dataset for climate and weather simulations, Journal of the Atmospheric Sciences, 73, 3541-3555, doi:10.1175/JAS-D-15-0355.1, 2016.

6. Feldman, D. R., W. D. Collins, R. Pincus, X. L. Huang, X. H. Chen, Far-infrared surface emissivity and climate, Proceedings of National Academy of Sciences, doi: 10.1073/pnas.1413640111, 2014.

5. Chen, X. H., X. L. Huang, M. G. Flanner, Sensitivity of modeled far-IR radiation budgets in polar continents to treatments of snow surface and ice cloud radiative properties, Geophysical Research Letters, doi:10.1002/2014GL061216, 41(18), 6530-6537, 2014.

4. Huang, X. L., X.H. Chen, G. L. Potter, L. Oreopoulos, J. N.S. Cole, D.M. Lee, N. G. Loeb, A global climatology of outgoing longwave spectral cloud radiative effect and associated effective cloud properties, Journal of Climate, 27, 7475-7492, doi:10.1175/JCLI-D-13-00663.1, 2014.

3. Chen, X.H., X.L. Huang, X. Liu, Non-negligible effects of cloud vertical overlapping assumptions on longwave spectral fingerprinting studies, 2013: JGR-Atmospheres, 118, 7309-7320, doi:10.1002/jgrd.50562.

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2. Huang, X.L., J. N.S. Cole, F. He, G.L. Potter, L. Oreopoulos, D.M. Lee, M. Suarez, N.G. Loeb, Longwave band-by-band cloud radiative effect and its application in GCM evaluation, 2013: Journal of Climate, 26(2), 450-467, doi:10.1175/JCLI-D-12-00112.1.

1. Huang, X.L., V. Ramaswamy, and M.Daniel Schwarzkopf, 2006: Quantification of the source of errors in AM2 simulated tropical clear-sky outgoing longwave radiation, Journal of Geophysical Research - Atmospheres, 111, D14107, doi:10.1029/2005JD006576.

Synergistic Activities· Book Chapter: R. Goody and X. Huang, Absorption and Thermal Emission, in the 2nd Edition of the Encyclopedia of the Atmospheric Sciences, edited by G. North, Academic Press, in publisher.· Editor for Journal of Climate since 2018· Chair for Atmospheric Radiation Committee of the AMS since 2019 · Satellite Mission Participations: Co-Is on followed selected missions: NASA PREFIRE (PI: Tristan L’Ecuyer Univ. Wisconsin); NASA LIBERA (PI: Peter Pilewskies, CU Boulder), ESA FORUM (PI: Luca Palchetti, Italy). Science Definition Team Member for NASA CLARREO mission. · Peer Reviewing ServicesPanelist for DOE 2015/2012 Merit Review, NASA 2009 ROSES, and NASA 2014 ROSES; Mail-in reviewer for NSF, NASA, British Natural Environment Research Council (NERC), University of Basilicata (Italy), New Zealand Deep South Initiative; Peer reviewer for Nature Geosciences, BAMS, JGR-Atmosphere, Journal of Climate, Journal of the Atmospheric Sciences, Geophysical Research Letters, Atmospheric Chemistry and Physics, IEEE TGRS, Journal of Applied Climatology, and Journal of Atmospheric and Oceanic Technology, Climate Dynamics, Applied OpticsMajor Awards and Honors

· 2015 AOSS outstanding faculty award· 2015 NASA Langley’s Henry J. E. Reid award· 2003 Princeton AOS postdoctoral fellowship

Relevant Invited Talks on Major International Conferences:1. Huang, X.L., Cloud Longwave Scattering: A Missing Link in Current Models for Realistic Atmosphere and Surface Radiative, AS43-A001, the 16th Asia Oceania Geosciences Society Annual Meeting, Singapore, July 28 – August 2, 2019.2. Huang, X.L., Challenges and opportunities in the far-IR remote sensing, HTu4C.1, 2019 Optical Society of America Hyperspectral Imaging and Sounding of the Environment Conference, San Jose, California, June 25-27, 2019. 3. Huang, X.L., Spectrum: an underutilized dimension in climate modeling and diagnostics, 2018 Fall AGU, A24A-01, December 10-14, 2018. 4. Huang, X.L, Far-IR Remote Sensing of Our Planet: Challenges and Opportunities, Session of Light Scattering and Radiative Transfer: Basic Research and Application, 2018 Progress in Electromagnetics Research Symposium, Toyama, Japan, August 1-4, 2018.5. Huang, X.L, On the Use of Hyperspectral Observations in Climate Studies: Unveiling a Hidden Dimension, HW2F.2, 2016 Optical Society of America Hyperspectral Imaging and Sounding of the Environment, Leipzig, Germany, Nov 14-17, 2016.

Related Conference and meeting organization• Chair of Hyperspectral Imaging and Sounding of Environment (HISE) 2015 conference organized

by OSA (Optical Society of America), Arrowhead, California.• Primary Convener and chair of special sessions in AGU (American Geophysical Union)

meetings• “Arctic Energy Balance and Relevant Atmosphere and Surface Processes: Current

Understanding and Challenges”, Fall 2018 meeting, D.C and Fall 2019 meeting, San Francisco.

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Identification of Potential Conflicts of Interest or Bias in Selection of Reviewers

Collaborators and Co-editors: H. Aumann (JPL), R. Bantges (Imperial College), C. Bellisario (U. Edinburgh), H. Brindley (Imperial College), E.-S. Chung (U. Miami), J. Cole (Env. Canada), W. Collins (LBL, DoE), Y. Deng (Gatech), S. DeSouza-Machado (UMBC), B. Drouin (JPL), D. Feldman (Lawrence Berkeley Lab), E. Fetzer(JPL), M. Flanner (Univ. of Michigan), H. Guo (GFDL), B. Kahn(JPL), S. Kato (NASA Langley), J. Kay (U. Colorado Boulder), C. Kuo (Lawrence Berkeley Lab), C.-P. Kuo (FSU), S. Leroy (AER), J.-N. Li (Canada), P. Lin (Princeton University), X. Liu (NASA Langley), N. G. Loeb (NASA Langley), Z. Luo (CUNY), Y Ming (GFDL), L. Oraiopoulos (NASA GSFC), L. Palchetti (Italy), W.-F. Pan (SSAI), P. Pilewskie (CU Boulder), R. Pincus (CU Boulder), V. Ramaswamy (FGDL), T. Ren (TAMU), B. Soden (U. Miami), G. Stephens (NASA JPL), L.L. Strow(UMBC), W.Y. Su (NASA Langley), G. L. Tang (TAMU), L. Tristan (U. Wisconsin), J.F. Wang (Gatech), B. Wielicki (NASA Langley), K. Wu (NIUST, China), P. Yang (TAMU), Q. Yue(JPL), F. Zhang (Fudan University, China), D. Zhou (NASA Langley)

Graduate and postdoctoral advisors: Yuk Yung (Caltech), V. Ramaswamy (GFDL)

Graduate and postdoctoral advisees: Xiuhong Chen (U. Michigan),Yi-Hsuan Chen (Princeton University), Huiwen Chuang (Taiwan ROC), Chongxing Fan, Fang Pan (China), Colten Peterson, L. Song (Institute of Atmospheric Physics, China), Chunpeng Wang (Amazon, Seattle) , Yan Xie, W.Z. Yang (I.M. Systems Group Inc.)

Co-Editors of the past 24 months: J. Chiang (UC Berkeley), T. DelSole (George Mason Univ), Min-Fang Ting (Columbia Univ)

28

Appendix 1: Biographical Sketch

Mark G. Flanner

Education and Training

• Postdoctoral Fellow, Advanced Study Program (ASP), National Center for Atmospheric Research, Boulder CO (2007 – 2009)

• Ph.D., Earth System Science, University of California, Irvine (2007)• B.S., Biomedical Engineering, University of Wisconsin, Madison (2002)

Research and Professional Experience

2015 – pres: Associate Professor, Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor MI, and dry appointment in the Department of Earth and

Environmental Sciences

2016 – 2017: Visiting scholar at the Institut des Géosciences de l'Environnement (IGE), Université Grenoble Alpes, France

2009 – 2015: Assistant Professor, Department of Atmospheric, Oceanic & Space Sciences, University of Michigan, Ann Arbor MI

Ten Publications Related to Proposed Topic

10. Cook, J. M., Tedstone, A. J., Williamson, C., McCutcheon, J., Hodson, A. J., Dayal, A., Skiles, M., Hofer, S., Bryant, R., McAree, O., McGonigle, A., Ryan, J., Anesio, A. M., Irvine-Fynn, T. D. L., Hubbard, A., Hanna, E., Flanner, M., Mayanna, S., Benning, L. G., van As, D., Yallop, M., McQuaid, J. B., Gribbin, T., and Tranter, M. (2020), Glacier algae accelerate melt rates on the south-western Greenland Ice Sheet,The Cryosphere, 14, 309–330, doi:10.5194/tc-14-309-2020.

9. Dang, C., Zender, C. S., and Flanner, M. G. (2019), Intercomparison and improvement of two-stream shortwave radiative transfer schemes in Earth system models for a unified treatment of cryospheric surfaces, The Cryosphere, 13, 2325–2343, doi:10.5194/tc-13-2325-2019, 2019.

8. Chen, Y.-H., X.L. Huang, X. H. Chen, and M. G. Flanner (2019), The Effects of Surface Longwave Spectral Emissivity on Atmospheric Circulation and Convection over the Sahara and Sahel, J. Climate,32(15), 4873-4890, doi:10.1175/JCLI-D-18-0615.1.

7. Huang, X., X. Chen, M. Flanner, P. Yang, D. Feldman, and C. Kuo (2018), Improved r epresentation of surface spectral emissivity in a global climate model and its impact on simulated climate , J. Climate, 31, 3711–3727, doi:10.1175/JCLI-D-17-0125.1.

6. Flanner, M. G., Huang, X., Chen, X., and Krinner, G. (2018), Climate response to negative greenhouse gas radiative forcing in polar winter, Geophys. Res. Lett., 45, 1997–2004, doi:10.1002/2017GL076668.

5. He, C., Flanner, M. G., Chen, F., Barlage, M., Liou, K. N., Kang, S., Ming, J., and Qian, Y. (2018), Black carbon-induced snow albedo reduction over the Tibetan Plateau: uncertainties from snow grain shape and aerosol–snow mixing state based on an updated SNICAR model, Atmos. Chem. Phys., 18, 11507-11527, doi:10.5194/acp-18-11507-2018.

4. Kuo, C.-P., Yang, P., Huang, X., Feldman, D., Flanner, M., Kuo, C., and Mlawer, E. J. (2017), Impact of multiple scattering on longwave radiative transfer involving clouds, J. Adv. Model. Earth Syst., 9,

29

Appendix 1: Biographical Sketch

3082–3098, doi: 10.1002/2017MS001117.

3. Chen, X., X. Huang, and M. G. Flanner (2014), Sensitivity of modeled far-IR radiation budgets in polar continents to treatments of snow surface and ice cloud radiative properties, Geophys. Res. Lett., 41, 6530–6537, doi:10.1002/2014GL061216.

2. Flanner, M. G., A. S. Gardner, S. Eckhardt, A. Stohl, J. Perket (2014), Aerosol radiative forcing from the 2010 Eyjafjallajökull volcanic eruptions, J. Geophys. Res. Atmos., 119, 9481–9491, doi: 10.1002/2014JD021977.

1. Lawrence, D., K. W. Oleson, M. G. Flanner, P. E. Thorton, S. C. Swenson, P. J. Lawrence, X. Zeng, Z.-L. Yang, S. Levis, K. Skaguchi, G. B. Bonan and A. G. Slater (2011), Parameterization Improvements and Functional and Structural Advances in Version 4 of the Community Land Model, J. Adv. Model. Earth Syst., 3, 27, doi:10.1029/JAMES.2011.3.45.

Synergistic Activities

1. Author and maintainer of the Snow, Ice, and Aerosol Radiative (SNICAR) model (available online at: http://snow.engin.umich.edu).

2. Co-chair of the Arctic Monitoring and Assessment Programme (AMAP) Short-Lived Climate Forcers (SLCF) expert panel, and a lead author of AMAP Technical Reports: The Impact of Black Carbon on Arctic Climate (2011) and Black carbon and ozone as Arctic climate forcers (2015).

3. Steering committee member for the snow model intercomparison project: ESM-SnowMIP4. Contributing author to the IPCC Fifth Assessment Report (2013) and Sixth Assessment Report

(forthcoming)5. Co-lead of U-M Undergraduate Expedition to Greenland (June 2019)

Identification of Potential Conflicts of Interest or Bias in Selection of Reviewers

Collaborators:

P. Alexander (Columbia U.), S. Arnold (U. Leeds), M. Bergin (Georgia Tech.), J. Cook (U. Sheffield), Z.Courville (U.S. Army CREEL), C. Dang (U. California - Irvine), J. Dibb (U. New Hampshire), A.Gardner (JPL), C. Golaz (LLNL), C. He (NCAR), D. Lawrence (NCAR), J. Kay (U. Colorado), G.Krinner (CNRS), C. Kuo (LBNL), C. Kuo (Texas A&M), K. Kupiainen (Finnish Ministry of theEnvironment), W. Lin (BNL), R. Mahmood (Environment Canada), H. Matsui (Nagoya U.), G. Myhre(CICERO), C. Polashenski (U.S. Army CREEL), P. Rasch (PNNL), M. Sand (CICERO), J. Schmale(PSI), M. Skiles (U. Utah), B. Smith (U. Washington), A. Soja (NIAA), A. Stohl (NILU, Norway), T. Sun(U. Illinois), M. Tedesco (Columbia U.), J. Thomas (Institut des Géosciences de l'Environnement), K. vonSalzen (Environment and Climate Change Canada), H. Wang (PNNL), S. Wang (Columbia U.), C. Wobus(ABT Consulting), P. Yang (Texas A&M).

Co-editors:

F. Dominé (CNRS, France), O. Eisen (Alfred-Wegener-Institut, Germany), C. Haas (York U., Canada), C. Hauck (U. Fribourg, Switzerland), T. Mölg (Erlangen-Nürnberg, Germany)

Graduate and Postdoctoral Advisors and Advisees

Graduate advisor: Charlie Zender (U. California – Irvine)

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Appendix 1: Biographical Sketch

Postdoctoral sponsor: Natalie Mahowald (Cornell U.) and Phil Rasch (PNNL)Graduate student advisees: Chaoyi Jiao (Facebook Inc), Justin Perket (NOAA GFDL), AdamSchneider (U. California – Irvine, Deepak Singh (PRI in India), Jamie Ward, Chloe WhickerPostdoctoral advisees: Alex Gardner (JPL), Yang Li (Harvard U), Ayoe Hansen (UK Met Office)

31

Appendix 1: Biographical Sketch

Ping YangProfessional Preparation Lanzhou University, Lanzhou, China; Theoretical Physics; B.S.; 1985Lanzhou Institute of Plateau Atmospheric Physics, Chinese Academy of Science, Lanzhou, China;

Atmospheric Physics; M.S.;1988

University of Utah, Salt Lake City, Utah, U.S.A.; Meteorology Ph.D.; 1995

Appointments University Distinguished Professor (2020-)Associate Dean for Research: (9/2019-2/2020, interim) and (3/2020-; formal appointment) Department Head, 09/2012-8/31/2018, Dept. of Atmospheric Sciences, Texas A&M UniversityJoint Professor Appointment, 06/2009-present, Dept. of Physics & Astronomy, Texas A&M UniversityProfessor, 09/2008-present, Dept. of Atmospheric Sciences, Texas A&M UniversityAssociate Professor, 09/2005-08/2008, Dept. of Atmospheric Sciences, Texas A&M UniversityAssistant Professor, 09/2001-08/2005, Dept. of Atmospheric Sciences, Texas A&M UniversityAssociate Research Scientist, 03/2001-09/2001, Goddard Earth Sciences and Technology Center,

University of Maryland Baltimore CountyStaff Scientist, 01/1999-02/2001, Science and System Application, Inc., Lanham, MarylandAssistant Research Scientist, 12/1997-01/1999, Dept of Atmospheric Sciences, University of California,

Los AngelesPostdoctoral Research Associate, 01/1996-11/1997, Dept. of Meteorology/Center for Atmospheric

Remote Sensing Study, University of Utah

Synergistic Activities · Member of the International Radiation Commission (IRC) for term 2012-2020.· Editor of the Journal of the Atmospheric Sciences (04/2015 - present); Associate editor, the

Journal of Quantitative Spectroscopy & Radiative Transfer (01/2007-present); Editorial Board, Remote Sensing of Environment (5/2015-present); and Editorial Board member/editor, Theoretical and Applied Climatology (09/2010-present); Editorial Board (11/2017-present), Remote Sensing; Associate Editor (1/2018-present), Journal of Geophysical Research-Atmospheres.

· Co-chair of Program Committee (2005, 2007, 2009, 2011), Hyperspectral Imaging and Sounding of Environment Topical Meeting sponsored by the Optical Society of America.

· Session co-organizer (2005, 2009, 2017), Progress in Electromagnetics Research Symposium.· Convener and Chairperson of an AGU sessions (2006, 2007,2017)

Publications (as of 3/4/2020): 4 books; 11 book chapters; 330 peer-reviewed articles; citations=11,736; h–index = 54

(http://www.researcherid.com/rid/B-4590-2011)

Selected papers: 6. Yang, P., J, Ding, R. L. Panetta, K.-N. Liou, G. W. Kattawar, M. I. Mishchenko, 2019: On the

convergence of numerical computations for both exact and approximate solutions for electromagnetic scattering by nonspherical dielectric particles, Progress In Electromagnetics Research, 164, 27-61 (invited paper).

32

Appendix 1: Biographical Sketch

5. Yang, P., S. Hioki, M. Saito, C.-P. Kuo, B. A. Baum, K.-N. Liou, 2018: A review of ice cloud optical property models for satellite remote sensing, Atmosphere, 9, 499; doi:10.3390/atmos9120499

4. Yang, P., et al., 2013: Spectrally consistent scattering, absorption, and polarization properties of atmospheric ice crystals at wavelengths from 0.2 to 100 µm. J. Atmos. Sci., 70, 330-347.

3. Ding, J., P. Yang, M. D. King, S. Platnick, X. Liu, K. Meyer, and C. Wang, 2019: A fast vector radiative transfer model for the atmosphere-ocean coupled system, J. Quant. Spectrosc. Radiat. Transfer, 239, https://doi.org/10.1016/j.jqsrt.2019.106667

2. Ren, T., P. Yang, G. Tang, X. Huang, and E. Mlawer, 2020: Improved delta-Eddington approximation for optically think clouds, J. Quant. Spectrosc. Radiat. Transfer, 240, https://doi.org/10.1016/j.jqsrt.2019.106694

1. Kuo, C.-P., P. Yang, X. Huang, Y.-H. Chen, and G. Liu, 2020: Assessing the accuracy and efficiency of longwave radiative transfer models involving scattering effect with cloud optical property parameterizations, J. Quant. Spectrosc. Radiat. Transfer, 240, https://doi.org/10.1016/j.jqsrt.2019.106683

Identification of Potential Conflicts of Interest or Bias in Selection of Reviewers

Collaborators of the past 48 months:S. Ackerman (CIMSS), B. Baum (Science and Technology Corporation), S. Brooks (Texas A&M), A. Dessler (Texas A&M), D. Feldman (LBL), Kevin Garrett (Texas A&M), Cenlin He (NCAR), Andrew Heidinger (UW/CIMSS), A. Heymsfield (NCAR), Robert Holz (U. of Wisconsin–Madison), C. Hsu (NASA GSFC), George Kattawar (Texas A&M), Michael King (University of Colorado), Norman Loeb (NASA Langley), Y. Li (University of Wisconsin), K.-N. Liou (UCLA), Quanhua Liu (NOAA/NESDIS), Xu Liu (NASA Langley), Eli Malwer (AER), Kerry Meyer (NASA GSFC), Patrick Minnis (NASA Langley), M. Mishchenko (NASA GISS), S. Platnick (NASA GSFC), Chenxi Wang (U. of Maryland, Baltimore County), Zhibo Zhang (UMBC), Penwang Zhai (UMBC)

Graduate and postdoctoral advisorsProf. Kuo-Nan Liou (Ph.D. advisor, and also Postdoctoral advisor), UCLA

Graduate advisor: Adam Bell, Dongchen Li, Jeffrey Mast, Nancy Okeudo, Yi Wang, Jian Wei, Yuheg ZhangPostdoctoral advisor: James Coy, Jiachen Ding, Tong Ren, Masanori Saito, and Steven Schroeder

Co-Editors of the past 24 months: M. Mischenko (Editor-in-Chief, J. Quantitative Spectroscopy & Radiative Transfer), Anne Smith (Editor-in-Chief, J. Atmos. Sci.), Prasad Thenkaball (Editor-in-Chief, Remote Sensing), Menghua Wang (Editor-in-Chief, Remote Sensing of Environment), and Minghua Zhang (Editor-in-Chief, JGR-Atmospheres)

33

Appendix 1: Biographical Sketch

Charles S. (Charlie) Zender

Departments of Earth System Science and Computer Science Voice: (949) 891-2429University of California, Irvine E-mail: zender@uci.eduIrvine, CA 92697-3100 Web: http://www.ess.uci.edu/~zender

a. PROFESSIONAL PREPARATIONHarvard University, Cambridge MA Physics B.A. 1990University of Colorado at Boulder Atmospheric Science Ph.D. 1996NCAR Advanced Studies Program Clouds & Aerosols Postdoc 1996–1998

b. APPOINTMENTS7/12– Professor of Computer Science, University of California, Irvine (UCI)7/10– Professor of Earth System Science, UCI9/10–8/13 Vice Chair of Graduate Studies, Department of Earth System Science, UCI7/05–6/10 Associate Professor of Earth System Science, UCI8/07–8/08 Visiting Researcher, Laboratoire de Glaciologie et Géophysique de l’Environnement (CNRS/LGGE), Grenoble, France3/00–2/06 Affiliate Scientist, Climate and Global Dynamics (CGD) Division, National Center for Atmospheric Research (NCAR), Boulder, CO7/99–6/05 Assistant Professor of Earth System Science, UCI7/98–9/99 Visiting Scientist, Atmospheric Chemistry and CGD Divisions, NCAR7/96–6/98 Postdoctoral Fellow, Advanced Studies Program, NCAR8/91–6/96 Graduate Research Assistant, University of Colorado at Boulder and NCAR

c. (i). FIVE PUBLICATIONS RELATED TO PROPOSED TOPIC (author is/was in our group: ***high school, **undergraduate, *graduate student, £post-doc, ǂresearcher)

*Laffin, M. K., C. S. Zender, S. Singh, J. M. van Wessem, C. J. P. P. Smeets, and C. H. Reijmer (2020), 40 Years of föhn wind-induced melt on the Antarctic Peninsula from 1979–2018, In Review in J. Geophys. Res.. (PDF)ǂWang, W., C. S. Zender, D. van As, R. S. Fausto, and M. .K Laffin (2020), Greenland surface melt dominated by solar and sensible heating, In Review in Nature Geosci.. (PDF)£Dang, C., C. S. Zender, *M. G. Flanner (2019), Intercomparison and improvement of two-stream shortwave radiative transfer schemes in Earth system models for a unified treatment of cryosphere surfaces, The Cryosphere, 13(9), 2325–2343, doi:10.5194/tc-13-2325-2019.(PDF)Evans, K. J., J. H. Kennedy, D. Lu, M. M. Forrester, S. Price, J. Fyke, A. R. Bennett, M. Hoffman, M. Vizcaino, I. Tezaur, and C. S. Zender (2019), LIVVkit 2.1: Automated and extensible ice sheet model validation, Geosci. Model Dev., 12, 1067–1086, doi:10.5194/gmd-2018-70. (PDF)Golaz, J.-C., P. M. Caldwell, L. P. Van Roekel, and 78 co-authors including C. S. Zender (2019), The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Syst., doi:10.1029/2018MS001603. (PDF)

c. (ii). FIVE ADDITIONAL PUBLICATIONS*Wang, W., C. S. Zender, D. van As, and N. B. Miller (2019), Spatial distribution of melt season cloud radiative effects over Greenland: Evaluating satellite observations, reanalyses, and model simulations against in situ measurements, J. Geophys. Res. Atm., 124, doi:10.1029/2018JD028919. (PDF)

34

Appendix 1: Biographical Sketch

*Wang, W., C. S. Zender, and D. van As (2018), Temporal Characteristics of Cloud Radiative Effects on the Greenland Ice Sheet: Discoveries from Multiyear Automatic Weather Station Measurements, J. Geophys. Res. Atm., 123, doi:10.1029/2018JD028540. (PDF)*Wang, W., C. S. Zender, D. van As, P. C. J. P. Smeets, and M. R. van den Broeke (2016), A Retrospective, Iterative, Geometry-Based (RIGB) tilt correction method for radiation observed by automatic weather stations on snow-covered surfaces: application to Greenland, The Cryosphere, 10, 727–741, doi:10.5194/tc-10-727-2016. (PDF)*Flanner, M. G., C. S. Zender, P. G. Hess, N. M. Mahowald, T. H. Painter, V. Ramanathan, and P. J. Rasch (2009), Springtime Warming and Reduced Snow Cover from Carbonaceous Particles, Atmos. Chem. Phys., 9(7), 2481–2497, http://www.atmos-chemphys.net/9/2481/2009. (PDF)*Flanner, M. G., C. S. Zender, J. T. Randerson, and P. J. Rasch (2007), Present-Day Climate Forcing and Response from Black Carbon in Snow, J. Geophys. Res., 112(D11), D11202, doi:10.1029/2006JD008003. (PDF)

d. SYNERGISTIC ACTIVITIES1. Author/maintainer: Justified Automatic Weather Station (JAWS) database of global polar

AWS measurements, 2018–present.2. Co-author and/or Contributor to three CMIP6 on-line living documents: 1) Specifications for

Regridding Weights (Google Docs), 2) Model Output Requirements: File Contents and Format, Data Structure and Metadata (Google Docs, section on compression), and 3) Output Grid Guidance (Google Docs, section 5 on regridding), 2018.

3. Co-author: NetCDF Climate and Forecast (CF) Metadata C onventions, 2019–present.4. Co-author/developer: SNow, ICe, and Aerosol Radiative model (SNICAR, SNICARAD),

2006–present.5. Author and maintainer, netCDF Operators (NCO), 1995–present.

e. IDENTIFICATION OF POTENTIAL CONFLICTS OF INTEREST OR BIAS IN SELECTION OF REVIEWERS

e. (i). Collaborators (Past 48 Months) and Co-Editors (24 Months)R. J. Allen (UCR), D. C. Bader (LLNL), S. M. Burrows (PNNL), P. M. Caldwell (LLNL), C. Dang (NOAA), E. Davis (Unidata), K. J. Evans (ORNL), M. G. Flanner (U. Michigan), R. S. Fausto (GEUS), J.-C. Golaz (LLNL), M. E. Gorris (LANL), X. Huang (U. Michigan), E. C. Hunke (LANL), R. Jacob (ANL), A. Jelenak (HDF), J. Kennedy (ORNL), S. J. S. Khalsa (NSIDC), P. Kuipers Munneke (IMAU), M. Lazzara (U. Wisc.), D. L. LeBauer (U. Arizona), J. Lenaerts (U. Colorado), P. J. T. Leonard (GSFC), C. S. Lynnes (GSFC), N. M. Mahowald (Cornell), J. K. Moore (UCI), S. Parajuli (KAUST), M. Prather (UCI), S. Price (LANL), J. T. Randerson (UCI), P. J. Rasch (PNNL), C. Reijmer (IMAU), A. Roberts (LANL) J. D. Silver (U. Melbourne), S. Singh (UCI), C. J. P. P. Smeets (IMAU), Q. Tang (LLNL), M. A. Taylor (SNL), K. K. Treseder (UCI), D. Williams (LLNL), D. van As (GEUS), M. R. van den Broeke (IMAU), J. M. van Wessem (IMAU), P. Yang (TAMU)

e. (ii). Graduate and Postdoctoral Advisors and AdviseesR. J. Allen (UCR), H. Bian (UMBC GEST/NASA GSFC), S. Capps (ADS), W. A. Cooper (NCAR), C. Dang (NOAA), E. Delman (UCLA), M. G. Flanner (U. Michigan), M. E. Gorris (LANL), A. Grini (UiO), Q. Han (Unknown), J. T. Kiehl (NCAR), M. K. Laffin (UCI), S. Parajuli (KAUST), A. Schneider (UCI), G. T. Thomas (CU), M. G. Tosca (SAIC), D. Wang (SLAC), W. Wang (UCI), X. Wang (Sun Yat-Sen Univ.), Z. Wolff (UCI)

35

Appendix 1: Biographical Sketch

Wuyin LinAtmospheric ScientistBrookhaven National Laboratory (BNL), Upton, NYEnvironmental and Climate Sciences DepartmentPhone: (631) 344-7249, Email: wlin@bnl.gov

Education and TrainingB.S. Atmospheric Science, 1988 Nanjing University, Nanjing, ChinaPh.D. Atmospheric Science, 2002 Stony Brook University, Stony Brook, NY

Research and Professional Experience 2013–Present: Atmospheric Scientist

BNL, Upton, NY. Current research efforts include 1) development, testing and evaluation of the Energy Exascale Earth System (E3SM) model, 2) high-resolution E3SM modeling and validation, 3) local-PI for CMDV-RRM project on investigation of cloud and convective processes across the scale in E3SM.

2011–2013: Associate Atmospheric ScientistBNL, Upton, NY. Climate model evaluation, development of climate metrics, cloud process modeling

2009–2011: Assistant Atmospheric ScientistBNL, Upton, NY. Development of fast physics testbed for model development and evaluation; regional climate modeling system development and simulations.

2002–2009: Research Assistant ProfessorStony Brook University, Stony Brook, NY. Cloud and Climate Modeling; Diagnostic study of marine boundary layer clouds; investigation of double ITCZ problem in coupled climate models.

2002–2006: Postdoc Research AssociateStony Brook University, Stony Brook, NY. Climate Modeling; development of cloud macrophysical scheme.

Selected Publicationsa. Xie, S., Y.-C. Wang, W. Lin, H.-Y. Ma, Q. Tang, S. Tang, X. Zheng, J.-C. Golaz,

G. J. Zhang, M. Zhang, 2019, Improved Diurnal Cycle of Precipitation in E3SM With a Revised Convective Triggering Function, JAMES, https://doi.org/10.1029/2019MS001702.

2. Lee, D. Y., M. Petersen and W. Lin, 2019, The Southern Annular Mode and Southern Ocean Surface Westerly Winds in E3SM, Earth and Space Science, https://doi.org/10.1029/2019EA000663.

3. Rasch, P., S. Xie, P.-L. Ma, W. Lin and coauthors, 2019, An Overview of the Atmospheric Component of the Energy Exascale Earth System Model, JAMES, https://doi.org/10.1029/2019MS001629.

4. Xie, S., W. Lin and coauthors, 2018, Understanding cloud and convective characteristics in version 1 of the E3SM atmosphere model, JAMES, doi: 10.1029/2018MS001350.

5. Lin, W., Y. Liu, A.M. Vogelmann, A.M. Fridlind, S. Endo, S. Feng, T. Toto, Z. Li, and M. Zhang, 2015: RACORO Continental Boundary Layer Cloud Investigations. Part III: Separation of Parameterization Biases in Single-Column Model CAM5 Simulations of Shallow Cumulus, J. Geophys. Res. Atmos., 120, DOI: 10.1002/2014JD022524.

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Appendix 1: Biographical Sketch

6. Song, H.,W. Lin, Y. Lin, A. B. Wolf, R. Neggers, L. J. Donner, A. D. Del Genio, and Y. Liu, 2014: Evaluation of Cloud Fractions Simulated by Seven SCMs against the ARM Observation at the SGP Site. J. Climate, 27, 6698-6719.

7. Song, H, W. Lin, Y. Lin, A. Wolf, R. Neggers, L. Donner, A. Del Genio, and Y. Liu, 2013: Evaluation of precipitation simulated by seven SCMs against the ARM observation at the SGP site. J. Climate, 26, 5467-5492.

8. Liu, H., M. Zhang, and W. Lin, 2012: An investigation of the initial development of the double ITCZ warm SST biases in the CCSM3, J. Climate, 25, 140-155, doi:10.1175/2011JCLI4001.1.

9. Liu, H., W. Lin, and M. Zhang, 2010: Heat budget of the upper ocean in the south central equatorial Pacific, J. Climate, 23,1779-1792,doi: 10.1175/2009JCLI3135.1.

10. Lin, W., M. Zhang, and J. Wu, 2009: Simulation of low clouds from the CAM and the regional WRF with multiple nested resolutions, Geophys. Res. Lett., 36, L08813, doi:10.1029/2008GL037088

Collaborators and Co-AuthorsPeter Caldwell (LLNL), Scott Collis (ANL), Satoshi Endo (BNL), Ann Fridlind (GISS), Chris Golaz (LLNL), Jorge Gonzalez (CUNY), Xianglei Huang (UM), Stephen Klein (LLNL), Doo-Young Lee (LANL), Hailong Liu (IAP),Ping Liu (SBU), Yangang Liu (BNL), Richard Neale (NCAR), Luis Oritz (CUNY), Mark Petersen(LANL), Yun Qian (PNNL), Phil Rasch (PNNL), Qi Tang (LLNL), Andrew Vogelmann (BNL), Hui Wan (PNNL), Hailong Wang (PNNL), Shaocheng Xie (LLNL), Haiyang Yu (SBU), Weihua Yuan (IAP), Kai Zhang (PNNL), Minghua Zhang (SBU), Tao Zhang (BNL), Yuying Zhang (LLNL)

Graduate and Postdoctoral Advisors: Minghua Zhang, Stony Brook University

Undergraduate and Graduate Advisees: None

37

Appendix 2: Current and Pending Support

Current and Pending SupportLead PI: Xianglei Huang (the University of Michigan) as of March21, 2020

Current Project

TitleThe inclusion of UM Spectral Flux data set into the AIRS Official Products ( UM F054169)

Direct Sponsor NASA JPLSponsor Award SubK No.1630798Total Award/Budget $64,985Date 6/5/2019 – 6/4/2020Role PIPerson Months/Yr 0.5Effort Type CalendarPI Xianglei HuangSponsor Contact Jared Lincoln, jared.m.lincoln@jpl.nasa.govWork performed/Overlaps/Synergies:

This project has no overlap or synergy with the proposed studies. It is to integrate the spectral OLR data we developed into the AIRS products.

TitleResponse of simulated climate to time-dependent spectral solar irradiance as observed at the TOA (UM F054897)

Direct Sponsor NASASponsor Award 80NSSC19K1098Total Award/Budget $39,858Date 8/1/2019 -7/31/2020Role PIPerson Months/Yr 0Effort Type CalendarPI Xianglei HuangSponsor Contact Dongliang Wu, dong.l.wu@nasa.govWork performed/Overlaps/Synergies:

This project has no overlap or synergy with the proposed study. It aims to study the impact of solar spectral irradiance on the climate.

TitleOn the use of AIRS and CrIS spectral observations and derived products in CERES EBAF data productions (F055106)

Direct Sponsor NASA LangleySponsor Award 80NSSC19K1472Total Award/Budget $360,798Date 9/1/2019 -8/31/2020Role PIPerson Months/Yr 0Effort Type CalendarPI Xianglei HuangSponsor Contact Norman Loeb, norman.g.loeb@nasa.govWork This project has no overlap or synergy with the proposed study. It is to

38

Appendix 2: Current and Pending Support

performed/Overlaps/Synergies:

provide supporting studies for NASA CERES surface radiation algorithms.

TitleIncorporate More Realistic Surface-atmosphere Radiative Coupling in E3SM (UM F051628)

Direct Sponsor Department of EnergySponsor Award DE-SC0019278Total Award/Budget $756,098Date 9/15/2018 – 9/14/2021Role PIPerson Months/Yr 0.25Effort Type CalendarPI Xianglei HuangSponsor Contact Dorothy Koch, Dorothy.koch@science.doe.govWork performed/Overlaps/Synergies:

Our proposed work can be deemed as a continuation from this study. Our proposed studies are built upon the current outcome from this study, but with a particular focus on the high-latitude radiative coupling and for the high-spatial-resolution simulation.

Title Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) (F052312)d Direct Sponsor University of Wisconsin/NASASponsor Award 80NSSC18K1485Total Award/Budget $748,960 (UM portion)Date 8/1/2018 – 7/31/2023Role Co-I/Institutional PIPerson Months/Yr 0.7Effort Type CalendarPI Tristan L’Ecuyer (Huang Institutional PI)Sponsor Contact Thomas Wagner, Thomas.wagner@nasa.govWork performed/Overlaps/Synergies:

This project has no overlap or synergy with the proposed study. It is a NASA earth-venture instrument mission.

TitleSpectral flux from multiple years of Aqua and SUOMI-NPP measurements: derivation, validation, and application in climate studies (UM F051262)

Direct Sponsor NASASponsor Award 80NSSC18K1033Total Award/Budget $551,193Date 6/5/2018 – 6/4/2021Role PIPerson Months/Yr 1Effort Type CalendarPI Xianglei HuangSponsor Contact David Considine, david.b.considine@nasa.govWork This project has no overlap or synergy with the proposed study. It is to

39

Appendix 2: Current and Pending Support

performed/Overlaps/Synergies:

derive spectral flux and cloud feedbacks from two NASA observations

Pending Projects

Title

Utilizing geostationary satellite observations to develop a next generation ice cloud optical property model in support of JCSDA Community Radiative Transfer Model (CRTM) and JPSS CAL/VAL( UM 20-PAF04400)

Direct Sponsor Texas A&M University/ NASAAnnouncement NNH19ZDA001N-ESROGSSTotal Award/Budget $93,753Date 7/1/2019 – 6/30/2021Role PIPerson Months/Yr 1.0Effort Type CalendarPI Xianglei HuangSponsor Contact Dr. Ping Yang, pyang@geos.tamu.eduWork performed/Overlaps/Synergies:

This project has no overlap or synergy with the proposed study. It is a radiative transfer study for geostationary data utilization.

Title Libera (UM 19-PAF07832) (Pending award)Direct Sponsor University of Colorado Boulder/NASASolicitation NNH17ZDA004O-EVC1Total Award/Budget $1,109,940Project Dates 04/21/2020End Date 01/20/2033Role Institutional PIPerson Months/Yr 0.5Effort Type CalendarPI Lead PI, Peter Pilewskie ( UM PI, Xianglei Huang)Sponsor Contact Peter Pilewskie, peter.pilewskie@lasp.colorado.eduWork performed/Overlaps/Synergies:

This project has no overlap or synergy with the proposed study. It is a mission for future earth radiation budget observations.

Title

Modeling the far-IR snow and ice spectral emissivity with 3-D solutions to Maxwell Equations: Partial coherent and full coherent approaches ( UM 1—PAF05779)

Direct Sponsor NASAAnnouncement NNH18ZDA001N-RSTTotal Award/Budget

$416,393

Program Dates 10/01/2019 – 9/30/2022

40

Appendix 2: Current and Pending Support

Role PIPerson Months/Yr 0.5Effort Type CalendarPI Xianglei HuangSponsor Contact Lucia Tsaoussi, lucia.s.tsaoussi@nasa.govWork performed/Overlaps/Synergies:

This project has no overlap or synergy with the proposed study. It is a pure radiative transfer theoretical study to model surface spectral emissivity.

Title

Collaborative Research: Understanding and Modeling Arctic Sea Ice Variability and Its Weather-Climate Implications with Improved Representations of Surface Energy Processes (UM 20-PAF03311)

Direct Sponsor NSFAnnouncement PD 06-5740Total Award/Budget $249,515 (UM amount)Project Dates 5/15/2020 – 5/14/2023Role Institutional PIPerson Months/Yr 0.1Effort Type CalendarPI Xianglei Huang, Lead PI, Jingfeng Wang (Georgia Tech)Sponsor Contact Dr. Eric DeWeaver, edeweave@nsf.govWork performed/Overlaps/Synergies:

This project has no overlap or synergy with the proposed study. It is to study a new scheme for turbulent heat flux.

Co-I: Prof. Mark Flanner (the University of Michigan)Current Support

Title CAREER: Linking Cryospheric Process Across Scales to Model Non-Linear Albedo Feedback

Source of Funding National Science Foundation Sponsor Award ARC 1253154Total Award/Budget $601,490Date 5/1/2013-4/30/2020Person Months/Yr 1.0 AYRole PISponsor Contact William Wiseman wwiseman@nsf.gov

Work performed and Overlaps/Synergies

Designed and constructed an instrument to measure snow optical grain size, studied impurity grain size feedbacks, and developed estimates of the global cryosphere radiative effect. Minimal overlap with proposed project.

TitleCollaborative Research: Closing the Gaps in Climate Models’ SurfaceAlbedo Schemes of Processes Driving the Darkening of the Greenland Ice

41

Appendix 2: Current and Pending Support

SheetDirect Sponsor NSFSponsor Award NA130AR4310096Total Award/Budget $101,561Date 5/1/2017-4/30/2021Person Months/Yr 1.0 AYRole PI

Work performed and Overlaps/Synergies

Exploring techniques to improve the modeling of bare ice albedo on the Greenland Ice Sheet. Synergy with proposed project includes developing radiative transfer models suitable for inclusion in ESMs to model snow and ice darkening from algae. Proposed work deals with E3SM whereas this item proposes to work with CESM.

Title Characterizing ICESat-2 green light penetration as a function of snow andice surface radiative properties

Direct Sponsor NASASponsor Award 80NSSC20K0062Total Award/Budget $149,996Date 11/1/2019-10/31/2021Person Months/Yr 0.9 AY

Work performed and Overlaps/Synergies

Exploring biases in ICESat-2 surface altimetry associated with multiple scattering of green light in snow. Working with Operation Ice Bridge data to characterize this bias over different types of cryospheric surfaces. No overlap with proposed project.

Pending Support Title Snow Albedo Test Bed Scoping StudiesDirect Sponsor Universities Space Research Association/NASAAnnouncement NNH19ZDA001N-THPTotal Award $10,000Date 1/1/2020-6/30/2021Person Months/Yr 0.03 AYWork performed and Overlaps/Synergies

Small project to scope out opportunities for improving the measurement and modeling of albedo in snow-covered regions. Minimal overlap with proposed project.

TitleQuantifying the coupled evolution of snow grain shape, size and radiativeproperties during the life cycle of snow and associated hydroclimaticeffects

Direct Sponsor University Corporation for Atmospheric Research/NASAAnnouncement NNH19ZDA001N-IDSTotal Award $125,000Date 6/1/2020-5/31/2023Person Months/Yr 0.5 AY

Work performed and Project to incorporate self-consistent representations of physical properties of ice

42

Appendix 2: Current and Pending Support

Overlaps/Synergiescrystals in the atmosphere and snowpack, and self-consistent representations of snow morphology evolution and snowpack optical properties. Minimal overlap with proposed project.

Title Understanding snow algae ecological traits for linkages to snow grainmetamorphosis in mid-latitude mountain environments

Direct Sponsor Lawrence Berkley National Laboratory/NASAAnnouncement NNH19ZDA001N-IDSTotal Award $66,235Date 5/15/2020-5/14/2023Person Months/Yr 0.7 AY

Work performed and Overlaps/Synergies

Project aims to improve understanding of the lifecycle of snow algae in mid-latitude mountain environments via in-situ measurements, detailed modeling, and remote sensing. Synergies with the proposed project include development of detailed process-based models that could be adapted for use in ESMs and development of remote sensing techniques that could provide data for verification of new ice algae modeling capabilities in ESMs.

Co-I: Prof. Ping Yang (Texas A&M)Current Support

Title Collaborative Research: Systematic evaluation and further improvement of present broadband radiative transfer modeling capabilities

Direct Sponsor NSFSponsor Award AGS-1632209Total Award/Budget $509,434 (TAMU portion)Program Dates 09/1/2016-8/31/2020Role PIPerson Months/Yr 0.5Effort Type CalendarPI Ping Yang (Co-I: Eli Mlawer)Work performed/Overlaps/Synergies:

This project aims to assess longwave scattering effect. There is no overlap between this project and the proposed DOE project.

Title Development of community light scattering computational capabilitiesDirect Sponsor NSFSponsor Award AGS-1826936Total Award/Budget $593,862Program Dates 09/01/2018-08/31/2021Role PIPerson Months/Yr 1.0Effort Type CalendarPI Ping Yang (Co-I: Richard Lee Panetta)Work performed/Overlaps/Synergies:

This project aims to develop new light scattering computational capabilities. There is no overlap between this project and the proposed

43

Appendix 2: Current and Pending Support

DOE project.

Title Incorporate more realistic surface-atmosphere radiative coupling in E3SMDirect Sponsor DOE (in response to DE-FOA-0001862)Sponsor Award DE-SC0019278 (Subaward from Univ. of Michigan: SUBK00009230)Total Award/Budget $242,606 (TAMU portion)Program Dates 08/1/2018-7/31/2021Role Co-IPerson Months/Yr 0.35 Cal Mo./Y1, 0.6 Cal Mo./Y2, and 0.56 Cal Mo./Y3Effort Type CalendarPI Xianglei Huang (Univ. of Michigan)Work performed/Overlaps/Synergies:

We assisted the PI’s team at the University of Michigan to incorporate longwave scattering effect in a radiative transfer submodel used in climate models. There is no overlap between this project and the proposed DOE project.

Title Optical property calculations and radiation parameterizations in support of CERES Science Team

Direct Sponsor NASASponsor Award 80NSSC19K1316Total Award/Budget $290,062Program Dates 10/01/2019-09/30/2022Role PIPerson Months/Yr 1.0Effort Type CalendarPI Ping YangWork performed/Overlaps/Synergies:

This project aims to develop novel snow and ice cloud optical property models for the applications of the CERES mission. There is no overlap between this project and the proposed DOE project.

TitleRefinement of the MODIS Cloud Optical Product in Synergy with Continued Development of a Full Suite of EOS-SNPP Cloud Continuity Algorithms + Atmosphere Discipline Team Leads

Direct Sponsor NASASponsor Award 80NSSC18K1516Total Award/Budget $70,523 (TAMU portion)Program Dates 10/1/2018-9/30/2020Role Co-IPerson Months/Yr 0.1Effort Type CalendarPI Steven PlatnickWork performed/Overlaps/Synergies:

This project aims to develop an efficient and accurate narrow-band radiative transfer model for MODIS and VIIRS based remote sensing applications. There is no overlap between this project and the proposed DOE project.

44

Appendix 2: Current and Pending Support

Title Quantification of the consistency of the choice of ice cloud models in forward retrieval and radiative forcing assessment

Direct Sponsor NASASponsor Award 80NSSC18K0845Total Award/Budget $431,106Program Dates 04/30/2017-04/29/2022Role PIPerson Months/Yr 0.5 Mo/Y1, 1.0 Mo/Y2, 1.0Mo/Y3, 0.54 Mon/Y4Effort Type CalendarPI Ping YangWork performed/Overlaps/Synergies:

This project aims to assess the impact of spectral consistency/inconsistency of a forward ice cloud model on the downstream remote sensing applications and radiative forcing assessments. There is no overlap between this project and the proposed DOE project.

Title Study of dust aerosol optical and microphysical properties based on combined spaceborne lidar and polarimetry

Direct Sponsor NASASponsor Award 80NSSC18K1426Total Award/Budget $150,000Program Dates 6/01/2018-5/31/2020Role PIPerson Months/Yr 0.2Effort Type CalendarPI Ping YangWork performed/Overlaps/Synergies:

This project aims to develop realistic dust optical properties for applications to remote sensing based on lidar and polarimetric observations. There is no overlap between this project and the proposed DOE project.

Pending Support

Title Modeling the far-IR snow and ice spectral emissivity with 3-D solutions to Maxwell Equations: Partial coherent and full coherent approaches

Direct Sponsor NASAAnnouncementTotal Award/Budget $118,493 (TAMU portion)Program Dates 2/1/2019-1/31/2022Role Co-IPerson Months/Yr 0.34 Mo/Y1, 0.31 Mo/Y2 and 0.28 Mo/Y3Effort Type CalendarPI Xianglei HuangWork performed This project aims to use partial-coherent and full-coherent 3-dimensional

45

Appendix 2: Current and Pending Support

/Overlaps/Synergies: solutions to Maxwell Equations to model the far-IR snow and ice spectral emissivity. There is no overlap between this project and the proposed DOE project.

Title Developing an accurate ice cloud optical property model for lidar-based active remote sensing applications

Direct Sponsor NASAAnnouncementTotal Award/Budget $196,984 (TAMU portion)Program Dates 2/1/2019-1/31/2021Role PIPerson Months/Yr 1.0Effort Type CalendarPI Ping YangWork performed/Overlaps/Synergies:

This project aims to develop an ice cloud model that leads to consistency between passive and active remote sensing applications. There is no overlap between this project and the proposed DOE project.

TitleUtilizing geostationary satellite observations to develop a next generation ice cloud optical property model in support of JCSDA Community Radiative Transfer Model (CRTM) and JPSS CAL/VAL

Direct Sponsor NASAAnnouncementTotal Award/Budget $459,907Program Dates 7/10/2020-7/09/2023Role PIPerson Months/Yr 1.0Effort Type CalendarPI Ping Yang (Co-I: Xianglei Huang)Work performed/Overlaps/Synergies:

This project aims to use the geostationary satellite observations to develop a next-generation ice cloud model for applications to the Community Radiative Transfer Model. There is no overlap between this project and the proposed DOE project.

Co-I: Prof. Charles Zender (University of California, Irvine)Current and Pending Support

Current Support Title Improving Greenland ice sheet surface melt in E3SMDirect Sponsor DOE Earth System Model Development and Analysis via LLNL/E3SMSponsor Award LLNL-B639667Total Award/Budget $245,798Program Dates 2/1/20–1/31/22Role PI

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Appendix 2: Current and Pending Support

Person Months/Yr 0.05 summerEffort TypePI Charles ZenderWork performed/Overlaps/Synergies:

This project is synergistic with the new proposal, though contains no overlaps. One task in this project is to activate the pond and bare-ice features of the unified cryospheric shortwave radiative transfer module SNICAR-AD (developed in a previous E3SM project) on ice-sheets. This proposed project will connect the five SNICAR-AD shortwave bands through the coupler to the atmosphere, allowing ice-sheets and the overlying atmosphere to exchange spectrally accurate heating and cooling fluxes. We expect to this to eliminate artifacts caused by the current mismatched solar spectral bins between the surface and atmosphere, and thus improve solar-induced surface melt.

Title Improving Ice Sheet Surface Mass Balance in E3SM Through Improvements to the Physical Snowpack Model

Direct Sponsor DOE Scientific Discovery through Advanced Computing (SciDAC)Sponsor Award LANL-520117Total Award/Budget $173,768Program Dates 1/8/19–9/30/21Role Co-IPerson Months/Yr 0.05 summerEffort TypePI S. Price (Los Alamos National Laboratory)Work performed/Overlaps/Synergies:

This project is synergistic with though does not overlap the new proposal. This project improves the physics (density, thermal conductivity, snow grain size) of and deepens the firn model in ELM and couples its SMB with the underlying E3SM ice sheet model (MALI). The improved surface snowpack characteristics will produce more accurate solar spectral fluxes. The new proposal will improve the surface radiation balance spectrally in both solar and infrared regions and thus also improve ice-sheet SMB.

Title Monitoring, Analysis, and Modeling Particulate Matter Air Quality in Borrego Springs

Direct Sponsor Borrego Water DistrictSponsor Award BWD-5581980Total Award/Budget $43,551Program Dates 1/1/20–12/31/20Role PIPerson Months/Yr 0.05 summerEffort TypePI Charles ZenderWork performed/Overlaps/Synergies:

None. This is a field project to study and mitigate dust effects on air quality in the Salton Sea region.

47

Appendix 2: Current and Pending Support

Title High-Performance Analysis and Regridding Support for E3SMDirect Sponsor DOE Earth System Model Development and Analysis via LLNL/E3SMSponsor Award LLNL-B632442Total Award/Budget $380,301Program Dates 12/4/18–6/30/20Role PIPerson Months/Yr 1.0 academic, 1.0 summerEffort TypePI Charles ZenderWork performed/Overlaps/Synergies:

This project has minor synergies and no overlaps with the proposed project. This project provides the flexible tools to accurately regrid surface fluxes and boundary datasets, and these tools and the knowledge gained from developing them will allow us to easily produce any new boundary datasets (e.g., for spectrally resolved surface emissivity) that E3SM v3 will need.

Title Incorporate more realistic surface-atmosphere radiative coupling in E3SMDirect Sponsor DOE Earth System Model Development and AnalysisSponsor Award DE-SC0019278Total Award/Budget $156,988Program Dates 9/15/18–9/14/21Role Co-IPerson Months/Yr 0.05 summerEffort TypePI Xianglei Huang (U. Michigan)Work performed/Overlaps/Synergies:

This project is strongly synergistic with the new proposal which is coordinated to leverage it without overlaps. We have developed a new spectrally resolved surface LW emissivity parameterization for MPAS-Seaice. The MPAS-Seaice module will be ready to hand-off to the new project for fully coupled (B-case) integration with the coupler and testing by summer 2020 when the new project starts. The final year of the current project at UCI will improve the MPAS Seaice thermodynamics to make surface temperatures consistent with the spectral emissivity of each surface type (snow, ice, ponds), and then we will write manuscripts based on our uncoupled (G-case) simulations.

Pending Support Title High-Performance Analysis and Regridding Support for E3SMDirect Sponsor DOE Earth System Model Development and Analysis via LLNL/E3SMAnnouncementTotal Award/Budget $431,582Program Dates 7/1/20–6/30/22Role PIPerson Months/Yr 1.0 academic, 1.0 summer

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Appendix 2: Current and Pending Support

Effort TypePI Charles ZenderWork performed/Overlaps/Synergies:

This renewal project, like its predecessor, has minor synergies and nooverlaps with the proposed project. This project provides the flexible tools to accurately regrid surface fluxes and boundary datasets, and these tools and the knowledge gained from developing them will allow us to easily produce any new boundary datasets (e.g., for spectrally resolved surface emissivity) that E3SM v3 will need.

Title Elements: Advanced Lossless and Lossy Compression Algorithms for netCDF Datasets in Earth and Engineering Sciences (CANDEE)

Direct Sponsor NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI)AnnouncementTotal Award/Budget $599,917Program Dates 7/1/20–6/30/23Role PIPerson Months/Yr 1.0 academic, 1.0 summerEffort TypePI Charles ZenderWork performed/Overlaps/Synergies:

None. This project develops software APIs for advance compressionalgorithms in netCDF datasets.

Title NASA-Oriented Improvements to and Maintenance and Support of the netCDF Operators (NCO) Toolkit

Direct Sponsor NASA Advancing Collaborative Connections for Earth System Science(ACCESS 2019)

AnnouncementTotal Award/Budget $1,430,943Program Dates 7/1/20–6/30/23Role PIPerson Months/Yr 3.0 academic, 1.0 summerEffort TypePI Charles ZenderWork performed/Overlaps/Synergies:

None. This project supports the software infrastructure, user-support, and documentation underlying the netCDF Operators toolkit. Improvements include NASA oriented features, weekly office hours/support telecons, regression testing reported to CDash, PyNCO wrappers, shifting from Source Forge to GitHub, and changing documentation from TEXInfo to Sphinx.

Title Refining the representations of high-latitude surface-atmosphere radiative coupling in the E3SM

Direct Sponsor DOE Earth System Model Development and AnalysisAnnouncementTotal Award/Budget $195,753

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Appendix 2: Current and Pending Support

Program Dates 9/1/20–8/31/23Role Co-IPerson Months/Yr 0.05 summerEffort TypePI Xianglei Huang (U. Michigan)Work performed/Overlaps/Synergies:

This project synergizes with multiple current and pending projects in our group. This project leverages our E3SM-related projects, and is coordinated to avoid overlaps. This project will (possibly re-bin and) exchange our new SNICAR-AD shortwave fluxes (already in ELM and MPAS-Seaice), and our new spectrally resolved longwave fluxes (developed in MPAS-Seaice) between the surface and atmosphere to improve both surface and atmosphere heating and mitigate temperature biases. This project will support implementation of our cryospheric spectral longwave fluxes from MPAS-Seaice into ELM (as we did with SNICAR-AD).

50

Appendix 3: References

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Cahalan, R. F., et al., 2005: The International Intercomparison of 3DRadiation Codes (I3RC): Bringing together the most advanced radiativetransfer tools for cloudy atmospheres, Bull. Am. Meteorol. Soc., 86(9),1275 – 1293.

Chen, X. H., X. L. Huang, M. G. Flanner, 2014: Sensitivity of modeled far-IR radiation budgets in polar continents to treatments of snow surface and ice cloud radiative properties, Geophysical Research Letters, doi:10.1002/2014GL061216, 41(18), 6530-6537.

Chen, X.H., X.L. Huang, N. G. Loeb, H. L. Wei, 2013: Comparisons of clear-sky outgoing far-IR flux inferred from satellite observations and computed from three most recent reanalysis products, J. Climate, 26(2), 478-494, doi:10.1175/JCLI-D-12-00212.1.

Chen, Y.-H., C.-P. Kuo, X. L. Huang, P. Yang, X. H. Chen, 2017: The influence of Cloud Longwave Scattering together with a state-of-the-art Ice Longwave Optical Parameterization in Climate Model Simulations, Abstract A31E-2238, 2017 Fall AGU meeting, New Orleans, Dec 11-15, 2017.

Chou, M.-D., K.-T. Lee, S.-C. Tsay, and Q. Fu, 1999: Parameterization for cloud longwave scattering for use in atmospheric models, J. Climate, 12, 159-169.

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Dang, C., C. S. Zender, and M. G. Flanner (2019), Intercomparison and improvement of two-stream shortwave radiative transfer schemes in Earth system models for a unified treatment of cryospheric surfaces, The Cryosphere, 13(9), 2325-2343, doi:10.5194/tc-13-2325-2019.

Deng, M., G. G. Mace, Z. Wang, and E. Berry, 2015: CloudSat 2C-ICE product update with a new Ze parameterization in lidar-only region, J. Geophys. Res. Atmos., 120, 12,198-12,208, doi:10.1002/2015JD023.

Evans, K.. F., 1998: The spherical harmonic discrete ordinate method for three-dimensional atmospheric radiative transfer, J. Atmos. Sci., 55, 429-446.

Flanner, M. G., Zender, C. S., Randerson, J. T., and Rasch, P. J., 2007: Present-day climate forcing and response from black carbon in snow, J. Geophys. Res., 112 , D11202, doi:10.1029/2006JD008003.

Fu, Q., 1996: An accurate parameterization of the solar radiative properties of cirrus clouds for climate models. J. Climate, 9, 2058-2082.

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Fu, Q., P. Yang, and W.B. Sun, 1998: An accurate parameterization of the infrared radiative properties of cirrus clouds for climate models. J. Climate, 11, 2223-2237.

Gounou, A., and R. J. Hogan, 2007: A sensitivity study of the effect of horizontal photon transport on the radiative forcing of contrails, J. Atmos. Sci., 64, 1706–1716.

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Han, D., 1999: Studies of Longwave Radiative Transfer Under Broken Cloud Conditions: Cloud Parameterizations and Validations. Ph.D. dissertation, University of Maryland, College Park, College Park, Maryland.

Han, D. and R.G. Ellingson, 1999: Cumulus cloud formulations for longwave radiation calculations, J. Atmos. Sci. , 56 , 837–851.

Heidinger, A. K., and S. K. Cox, 1996: Finite-cloud effects in longwave radiative transfer. J. Atmos. Sci., 53, 953–963.

Held, I. M., 2005: The gap between simulation and understanding in climate modeling, Bull. Am. Meteorol. Soc., 86, 1609-1614.

Hogan, R. J., S. A. K. Schäfer, C. Klinger, J. C. Chiu, and B. Mayer, 2016: Representing 3-D cloud radiation effects in two-stream schemes: 2. Matrix formulation and broadband evaluation, J. Geophys. Res. Atmos., 121, 8583–8599.

Hogan, R. J., and A. Bozzo, 2016: ECRAD: A new radiation scheme for the IFS. ECMWF Technical Memoranda. Online is available at https://www.ecmwf.int/sites/default/ files/elibrary/2016/16901-ecrad-new-radiation-scheme-ifs.pdf.

Huang, X.L., N.G. Loeb, and W.Z. Yang, 2010: Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation, Part II: cloudy sky and band-by-band cloud radiative forcing over the tropical oceans, JGR-Atmospheres, 115, D21101, doi:10.1029/2010JD013932.

Huang, X.L., W.Z. Yang, N.G. Loeb, and V. Ramaswamy, 2008: Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation, Part I: clear sky over the tropical oceans, Journal of Geophysical Research Atmospheres, 113, D09110, doi:10.1029/2007JD009219.

Huang, X. L., X.H. Chen, D. K. Zhou, X. Liu, 2016: An observationally based global band-by-band surface emissivity dataset for climate and weather simulations, Journal of the Atmospheric Sciences, 73, 3541-3555, doi:10.1175/JAS-D-15-0355.1.

Huang, X. L., X.H. Chen, G. L. Potter, L. Oreopoulos, J. N.S. Cole, D.M. Lee, N. G. Loeb, 2014: A global climatology of outgoing longwave spectral cloud radiative effect and associated effective cloud properties, J. Climate, 27, 7475-7492, doi:10.1175/JCLI-D-13- 00663.1.

Huang, X. L., X. H. Chen, M. G. Flanner, P. Yang, D. Feldman, C. Kuo, 2018: Improved representation of surface spectral emissivity in a global climate model and its impact on simulated climate, J. Climate, in press (Early Online Release at https://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-17-0125.1; a preprint available at https://goo.gl/kQf2rv).

Jiao, C., M. Flanner, 2015: Parameter sensitivity study of Arctic aerosol vertical distribution in CAM5, 2015 Fall Meeting, AGU, San Francisco, California, December 14-18th, 2015.

Jones, P.D., M. New, D.E. Parker, S. Martin, and I.G. Rigor, 1999: Surface air temperature and its variations over the last 150 years, Reviews of Geophysics, 37, 173-199, doi:10.1029/1999RG900002.

Klinger et al., 2017: Effects of 3-D thermal radiation on the development of a shallow cumulus cloud field, Atmos. Chem. Phys., 17, 5477–5500.

Kuo, C.-P., P. Yang, X. L. Huang, D. Feldman, M. Flanner, C. Kuo, E. Mlawer, 2017: Impact of Multiple Scattering on Longwave Radiative Transfer Involving

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Appendix 3: References

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Loeb, N.G., B.A. Wielicki, D.R. Doelling, G.L. Smith, D.F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Towards optimal closure of the Earth's top-of atmosphere radiation budget, J. Climate, 22, 746-766.

Meier, W., F. Fetterer, M. Savoie, S. Mallory, R. Duerr, and J. Stroeve, 2013: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration. Version 2. Boulder, Colorado, USA: National Snow and Ice Data Center in association with the NOAA National Climatic Data Center (NCDC) under the NCDC Satellite Climate Data Record Program: doi.org/10.7265/N55M63M1.

O’Brien, D.M., L.J. Rikus, A.C. Dilley, and M. Edwards, 1997: Spectral analysis of infrared heating in clouds computed with two-stream radiation codes, J. Quant. Spectrosc. Radiat. Transfer, 57, 725-737.

Pincus, R., K. Evans, 2009: Computational cost and accuracy in calculating three dimensional radiative transfer: results for new implementations of Monte Carlo and SHDOM, J. Atmos. Sci., 66, 3131-3146.

Platnick, S., et al., 2017: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua, IEEE Trans. Geosci. Remote Sens., 55(1), 502–525.

Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res., 108, No. D14, 4407 doi:10.1029/2002JD002670.

Seemann, S.W., E. E. Borbas, R. O.Jon Knuteson, G. R. Stephenson, H.-L. Huang, 2008: Development of a Global Infrared Land Surface Emissivity Database for Application to Clear Sky Sounding Retrievals from Multi-spectral Satellite Radiance Measurements, Journal of Applied Meteorology and Climatology, 47,108–123. doi.org/10.1175/2007JAMC1590.1.

Schäfer, S. A. K., R. J. Hogan, C. Klinger, J. C. Chiu, and B. Mayer, 2016: Representing 3D cloud-radiation effects in two-stream schemes: 1. Longwave considerations and effective cloud edge length, J. Geophys. Res. Atmos., 121, doi:10.1002/2016JD024876.

Takara, E.E. and R.G. Ellingson, 2000: Broken cloud field longwave scattering effects, J. Atmos. Sci., 57, 1298–1310.

Warren, S. G., and Brandt, R. E., 2008: Optical constants of ice from the ultraviolet to the microwave: A revised compilation, J. Geophys. Res., 113, D14220, doi: 10.1029/2007JD009744.

Wolff, Z., and C. S. Zender (2020), Realistic Representation of Cryospheric Surface Emissivity Improves Atmospheric Longwave Radiation in Earth System Models, Submitted to J. Geophys. Res..

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Appendix 3: References

Zhou, D. K., A. M. Larar, X. Liu, W. L. Smith, L. L. Strow, P. Yang, P. Schlüssel, and X. Calbet, 2011: Global Land Surface Emissivity Retrieved from Satellite Ultraspectral IR Measurements, IEEE Transactions on Geoscience and Remote Sensing, 49, 1277–90.

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Appendix 4: Facilities & other resources/Appendix5: Equipment/Appendix6: Data Management and Sharing Plans

Facilities and other resourcesUniversity of Michigan: The UM team has access to DoE supercomputing facilities under current E3SM project (e.g. Cori), mainly for carrying out production runs. High-performance computing cluster at University of Michigan (http://arc.research.umich.edu/flux/) is also available for the proposed studies, primarily for data analysis and scheme developments. The cluster is maintained by the University and equipped with all mainstream scientific computing software and programming languages. The Michigan team has dedicated queue and allocated CPUs for carrying out such simulations. The PI’s group also has ~60TB storage for storing model simulations and data sets needed for model evaluations. No additional facilities or resources are needed for this study.

Texas A&M University: The team at Texas A&M University does not require any hardware purchase for this project. To accomplish the research tasks assigned to the Texas A&M team, they will use currently available computing resources in Prof. Yang’s group and in Texas A&M to perform the tasks.

University of California, Irvine: This project requires a modern high-speed personal computer for scientific software development (to be requested), and access to DOE supercomputing resources. The improved radiative treatment will be tested in E3SM on heterogeneous DOE systems that the UCI group can already access. These include supercomputers at ALCF, OLCF, and NERSC.

EquipmentBesides those facilities and resources described in Appendix 4, UCI team has budgeted a MacBookPro Workstation for developing and testing the fully spectrally coupled E3SM at low resolution. No other teams request additional equipment for the proposed study.

Data Management and Sharing Plans

In addition to make all schemes and codes available to the E3SM community, we will also make them available to the public for free access. The lead PI and his group have maintained a few Github websites hosting relevant codes and dataset. We will continue making the code of our modified schemes publicly available on GitHub.com, the popular software development platform offering all of the distributed version control and source code management. Currently the general codes useful for the entire community are available via https://github.com/Huang-Group-UMICH and the codes tailored for E3SM v2 are available via https://github.com/jingxianwen/E3SM_v2_alpha.

55

Appendix7: Glossary of Acronyms

Glossary of Acronyms2SD two-stream DISORTACME Accelerated Climate Model for EnergyAGCM Atmospheric General Circulation Model AGU American Geophysical UnionAIRS Atmospheric Infrared SounderALCF Argonne Leadership Computing FacilityALM ACME Land ModelAMS American Meteorological SocietyASR Atmospheric System Research ARM Atmospheric Radiation MeasurementARMBE ARM Climate Modeling Best Estimate Cloud RadiationAWS Amazon Web ServicesBER Biological and Environmental ResearchBNL Brookhaven National LaboratoryBSRN Baseline Surface Radiation NetworkCAM Community Atmospheric Model CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite ObservationCAPT Cloud Associated Parameterization TestbedsCCCma Canadian Centre for Climate Modelling and AnalysisCESM Community Earth System ModelCERES Clouds and Earth’s Radiant Energy Systems CERES EBAF Clouds and Earth’s Radiant Energy Systems Energy Balanced and Filled CICE Community Ice ModelCLM Community Land ModelCloudSat Satellite-based Cloud ExperimentCLUBB Cloud Layers Unified By BinormalsCOSP CFMIP Observation Simulator PackageCRE Cloud Radiative EffectDISORT Discrete Ordinates Radiative Transfer ProgramDOE U.S. Department of EnergyE3SM Energy Exascale Earth System ModelEAM E3SM Atmospheric ModelECMWF European Center for Medium Range Weather ForecastingECRAD ECMWF radiation scheme

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Appendix7: Glossary of Acronyms

ESM Earth System ModelingFar-IR Far InfraredFIR Far InfraredFOA Funding Opportunity Announcement GCM General Circulation Model GEWEX Global Energy and Water Cycle Exchanges Project GFDL Geophysical Fluid Dynamics LaboratoryGPCP Global Precipitation Climatology ProjectGISS E2-H/E2-R NASA Goddard Institute for Space Studies E2-H/E2-R modelGOAMAZON Observations and Modeling of the Green Ocean AmazonHPSS High-Performance Storage SystemI3RC International Intercomparison of 3D Radiation CodeIASI Infrared Atmospheric Sounding InterferometerICA Independent Column ApproximationIFS Integrated Forecast SystemIO Input and OutputIOP Intensive observation periodIPCC AR5 Intergovernmental Panel on Climate Change, the Fifth Assessment ReportIR InfraredISCCP International Satellite Cloud Climatology ProjectITCZ Inter Tropical Convergence ZoneLLNL Lawrence Livermore National LaboratoryLW LongwaveMAM Modal Aerosol ModelMODIS Moderate-resolution Imaging SpectroradiometerMPAS-seaice Model for Prediction Across Scales Sea IceMYSTIC Monte carlo code for the phYSically correct Tracing of photons In Cloudy atmospheresNASA National Aeronautics and Space AdministrationNCEP National Centers for Environmental Prediction NERSC National Energy Research Scientific Computing CenterNOAA National Oceanic and Atmospheric AdministrationNSA North Slope of AlaskaNSIDC National Snow and Ice Data CenterOLCF Oak Ridge Leadership Computing FacilityOLR Outgoing Longwave RadiationRAM Random Access Memory

57

Appendix7: Glossary of Acronyms

RRTMG_LW Longwave broadband Rapid Radiative Transfer Model for General Circulation ModelRRTMG_SW Shortwave broadband Rapid Radiative Transfer Model for General Circulation ModelRRTMG Rapid Radiative Transfer Model for General Circulation ModelSARB Surface and Atmospheric Radiation BudgetSciDAC Scientific Discovery through Advanced ComputingSCM Single Column ModelSCAM Single Column CAMSGP Southern Great PlainsSHDOM Spherical Harmonic Discrete Ordinate MethodSMB Surface Mass BalanceSNICAR Snow, Ice, and Aerosol Radiative model (snow only)SNICAR Snow, Ice, and Aerosol Radiative modelSNICAR-AD SNICAR Adding-Doubling (treats snow, bare ice, and ponds)SPARTACUS Speed Algorithm for Radiative Transfer through Cloud SidesSST Sea Surface TemperatureSW Shortwave TAMU Texas A&M UniversityTOA Top of AtmosphereTRMM Tropical Rainfall Measuring MissionTWP Tropical Western PacificUCI University of California, IrvineUCLA-LES University of California, Los Angeles large-eddy

simulationUK United Kingdom

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