Observations and Future Projections (Scientific basis)

1. Introduction: indicators of climate change and treatment of uncertainties2. Observations: atmosphere, ocean and surface 3. Detection and attribution of climate change: from global to regional

Jose A. MarengoHead, Research and Development

CEMADENjose.marengo@cenaden.gov.br

Sources of climate forcing

Medieval Warm Period and Little Ice Age: Natural climate change

Evidences of Little Ice Age

Causes of Little Ice Age

Explosive Volcanic

Eruptions: Proof of

Fast-Response Climate

Change Due to Forcing

Changing forcing

changes the

temperature (and

water vapor, etc.).

If volcanoes can cool,

then GHG must

warm….

Human causes of climate change

Human causes of climate change

Human causes of climate change

Human causes of climate change

Detection and attribution as forensics

One global climate model's reconstruction of temperature change during the 20th century as the result of five studied forcing factors and the amount of temperature change attributed to each

Attribution of climate change

Attribution of recent climate change is the effort to scientifically ascertain mechanisms responsible for recent climate changes on Earth, commonly known as 'global warming'. The effort has focused on changes observed during the period of instrumental temperature record, when records are most reliable; particularly in the last 50 years, when human activity has grown fastest and observations of the troposphere have become available. The dominant mechanisms are anthropogenic, i.e., the result of human activity.

increasing atmospheric concentrations of greenhouse gasesglobal changes to land surface, such as deforestationincreasing atmospheric concentrations of aerosols.

There are also natural mechanisms for variation including climate oscillations, changes in solar activity, and volcanic activity.

According to the Intergovernmental Panel on Climate Change (IPCC), it is "extremely likely" that human influence was the dominant cause of global warming between 1951 and 2010.[4] The IPCC defines "extremely likely" as indicating a probability of 95 to 100%, based on an expert assessment of all the available evidence.[5]

Multiple lines of evidence support attribution of recent

climate change to human activities:

A basic physical understanding of the climate system: greenhouse gas concentrations have increased and their warming properties are well-established.

Historical estimates of past climate changes suggest that the recent changes in global surface temperature are unusual.

Computer-based climate models are unable to replicate the observed warming unless human greenhouse gas emissions are included.

Natural forces alone (such as solar and volcanic activity) cannot explain the observed warming.

(Top) The variations of the observed global mean surface temperature anomaly from Hadley Centre/Climatic Research Unit gridded surface temperature data set version 3 (HadCRUT3, black line) and the best multivariate fits using the method of Lean (red line), Lockwood (pink line), Folland(green line) and Kaufmann (blue line). (Below) The contributions to the fit from (a) El Niño-Southern Oscillation (ENSO), (b) volcanoes, (c) solar forcing, (d) anthropogenic forcing and (e) other factors (Atlantic Multi-decadal Oscillation (AMO) for Folland and a 17.5-year cycle, semi-annual oscillation (SAO), and Arctic Oscillation (AO) from Lean).

Human influence has been detected in warming of the atmosphere and the ocean, in changesin the global water cycle, in reductions in snow and ice, in global mean sea level rise, andin changes in some climate extremes. This evidence for human influence has grown since AR4. It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century.

Human influence has been detected in warming of the atmosphere and the ocean, in changes in the global water cycle, in reductions in snow and ice, in global mean sea level rise, and in changes in some climate extremes. This evidence for human influence has grown since AR4. It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century.

Attribution studies of global zonal mean terrestrial precipitation and Arctic precipitation both find a detectable anthropogenic influence. Overall there is medium confidence in a significant human influence on global scale changes in precipitation patterns, including increases in NH mid-to-high latitudes.

Several new attribution studies have found a detectable anthropogenic influence in the observed increased frequency of warm days and nights and decreased frequency of cold days and nights.

Human influence has been detected in nearly all of the major assessed components of the climate system.

there is low confidence in attribution of changes in tropical cyclone activity to human influence. This is due to insufficient observational evidence, lack of physical understanding of the links between anthropogenic drivers of climate and tropical cyclone activity, and the low level of agreement between studies as to the relative importance of internal variability, and anthropogenic and natural forcings.

Detection and Attribution of Climate Change

Owing to the low confidence in observed large-scale trends in dryness combined with difficulties in distinguishing decadal-scale variability in drought from long-term climate change, there is now low confidence in the attribution of changes in drought over global landsince the mid-20th century to human influence.

Changes in the water cycle remain less reliably modelled in both their changes and their internal variability, limiting confidence in attribution assessments.

The ability to simulate changes in frequency and intensity of extreme events is limited by the ability of models to reliably simulate mean changes in key features.

Attribution of changes in monsoon to human influence generally has low confidence

Further evidence has accumulated of the detection and attribution of anthropogenic influence on temperature change in different parts of the world

Physical understanding is required to assess what constitutes a plausible discrepancy above that expected from internal variability.Even with complete consistency between models and data, attribution statements can never be made with 100% certainty because of the presence of internal variability.

Detection and Attribution of Climate Change

1. Observations of one or more climate variables, such as surface temperature, that are understood, on physical grounds, to be relevant to the process in question

2. An estimate of how external drivers of climate change have evolved before and during the period under investigation, including both the driver whose influence is being investigated (such as rising GHG levels) and potential confounding influences (such as solar activity)

3. A quantitative physically based understanding, normally encapsulated in a model, of how these external drivers are thought to have affected these observed climate variables

4. An estimate, often but not always derived from a physically based model, of the characteristics of variability expected in these observed climate variables due to random, quasi-periodic and chaotic fluctuations generated in the climate system that are not due to externally driven climate change

There are four core elements to any detection and attribution study:

Source: Trenberth et al., 2015

Attribution of climate extreme events

• Climate change is no doubt altering the atmosphericcirculation, but the change is relatively small and canonly be discerned from a very large ensemble ofmodel runs. That sets the change in odds.

• But for any event, the particular character of thatstorm or synoptic situation and natural variabilityrule, while thermodynamic effects increase theimpacts.

• Attributing an event solely to either human-induced climate change or naturalvariability can be misleading when both are invariably in play.

• The conventional attribution framework struggles with dynamically drivenextremes because of the small signal-to-noise ratios and often uncertain nature ofthe forced changes.

• It is more useful to regard the extreme circulation regime or weather event as beinglargely unaffected by climate change, and question whether known changes in theclimate system’s thermodynamic state affected the impact of the particular event.

• The ‘snowmaggedon’ in February 2010 in Washington DC, superstorm Sandy inOctober 2012 and supertyphoon Haiyan in November 2013, and the Boulder floodsof September 2013, all of which were influenced by high sea surface temperaturesthat had a discernible human component.

Attribution of climate extreme events

Attribution of climate extreme events

Research is pursued worldwide that aims to determine if a particular observed extreme event has become more or less likely due to climate change. A recent paper (King et al 2015) uses two methods to quantify how much more likely a record hot year in Central England has become. One of the methods is based largely on climate modeling, the other on interpreting the observed record. This is an important step towards improving the reliability of event attribution results. Improved understanding and prediction of changes in extreme events is recognized as one of the‘grand challenges’ in climate research.

Extreme weather and climate events demonstrate the vulnerability of society and ecosystems, and bring climate change into the public’ s interest far more thanchanges in global mean temperature do. Hence research that determines if a particular observed extreme event has become more or less likely due to climate change, and by how much, is pursued world-wide (Peterson et al 2014).

Global, land, ocean and continental annual mean temperatures for CMIP3 and CMIP5 historical (red) and historical Natural (blue) simulations (multi-model means shown as thick lines, and 5 to 95% ranges shown as thin light lines) and for Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4, black).

Time series of global and annual-averaged surface temperature change from 1860 to 2010. The from two ensemble of climate models driven with just natural forcings and driven with both natural forcing and human-induced changes in greenhouse gases and aerosols. Spatial patterns of local surface temperature trends from 1951 to 2010 from CMIP5 simulations driven with just natural forcings, with natural + human forcings and observed trends from theHadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4) during this period.

Comparison of observed global ocean heat content for the upper 700 m (updated from Domingues et al. 2008) with simulations from ten CMIP5 models that included only natural forcings (‘HistoricalNat’ runs shown in blue lines) and simulations that included natural and anthropogenic forcings (‘Historical’ runs in pink lines). Grey shading shows observational uncertainty. The global mean stratospheric optical depth (Sato et al., 1993) in beige at the bottom indicates the major volcanic eruptions and the brown curve is a 3-year running average of these values.

Time series of projected temperature change shown at four representative locations for summer and winter. Each time series is surrounded by an envelope of projected changes yielded by 24 different CMIP5 model simulations, emerging from a greyenvelope of natural local variability simulated by the models using early 20th century conditions. The warming signal emerges first in the tropics during summer. Thecentral map shows the global temperature increase (°C) needed for temperatures in summer at individual locations to emerge from the envelope of early 20th centuryvariability.

Detection and attribution signals in some elements of the climate system at global scales (bottom four panels). Brown panels are land surface temperature time series, green panels are precipitation time series, blue panels are ocean heat content time series and white panels are sea ice time series. Observations are shown on each panel in black or black and shades of grey. Blue shading is the model time series for natural forcing simulations and pink shading is the combined natural and anthropogenic forcings. The dark blue and dark red lines are the ensemble means from the model simulations. All panels show the 5 to 95% intervals of the natural forcing simulations, and the natural and anthropogenic forcing simulations.

Point-wise linear trend over the 1951–2014 period annual maximum daily high temperature (TXx) using GHCNDEX on a 2.5°2.5° latitude/longitude grid Units: °C/ ear, stippling indicates statistically significant trends(p⩽0.05).

Easterling et al (2016)

Pointwise linear trend over the 1951–2014 period in annual maximum 5 day total precipitation (Rx5day) using GHCNDEX on a 2.5°2.5 latitude/longitude grid. Units: mm year, stippling indicates statistically significant trends(p⩽0.05).

Easterling et al (2016)

Pointwise linear trend over the 1951 2014 period in the annual consecutive dry days(CDD) using GHCNDEX on a 2.5°2.5° latitude/longitude grid. Units:days year, stippling indicates statistically significant trends(p⩽0.05).

Easterling et al (2016)

Trend (%/decade) for the period of 1951–2014 in the number of 5- day duration cold spells with mean temperature less than the threshold for a 1-in-5yr recurrence. Grid box(4°4°) averages calculated from 5084 stations with less than 10% missing daily temperature data for 1951–2014. Grid boxes with statistically significant trends (computed with non parametric Mann-Kendall test)identified with white dots. Data used are from the Global Historical Climatology Network-Daily dataset (Menne etal., 2011).

Easterling et al (2016)

Probability distribution functions of the trends in two extreme temperature metrics for the coterminous United States for 1956 2005. Metrics include (a) annual maximum value of daily maximum temperature; and (b) annual minimum value of daily minimum temperature. The CMIP5 model trend distributions are shown for 7 8historical forcing (natural and anthropogenic) simulations from 29 CMIP5 models (red) and for 35 natural forcing only simulations from 15 CMIP5 models (blue).The observed U.S. trend is shown in gray.

Easterling et al (2016)

Estimated trend of annual mean temperatures (°C change over 45 yr) over China under different forcings during the period 1961−2005. OBS: observed trend; ALL: includes anthropogenic and natural external forcings; NAT: includes only solar irradiance and volcanic activity; GHG: greenhouse gases; LU: land use change; ANT: anthropogenic influences

Xu et al (2015)

Time-series of global mean precipitation anomalies (mm d-1) with respect to the baseline period of 1961–1990, simulated by CMIP5 models forced with, both anthropogenic and natural forcings (All; orange/red lines) and natural forcings only (Nat., blue lines). land and ocean (a), land (b) and ocean (c) with all grid points. Multi-model means are shown in thick solid lines. Green stars show statistically significant changes at 5% level.

Sarojini et al (2016)

Annual mean temperature anomalies averagedacross China from observation and model simulations during the period of 1961−2005. Shaded bands: multi-modelrange. OBS: observed trend; ANT: anthropogenic influ ences; NAT: includes only solar irradiance and volcanic ac-tivity; GHG: greenhouse gases; AA: anthropogenic aerosols; LU: land use change

Xu et al (2015)

http://www.ametsoc.net

Socioeconomic development interacts with

natural climate variations and human-caused

climate change to influence disaster risk

Increasing vulnerability, exposure,

or severity and frequency of

climate events increases disaster

risk

Social vulnerability and exposure are key determinants of disaster risk and help explain why non-extreme physical events and chronic hazards can also lead to extreme impacts and disasters, while some extreme events do not.

Key riskReduced access to water dor fural and urban poor people due to water scarcity and increasing

competition for water (high confidence)

Present

Near-term(2030-2040)

Long-term(2080-2100)

Verylow

Medium

2oC

4oC

Veryhigh

Future Climate Changes, Risks and Impacts Water, food and urban systems, human health, security and livelihoods

Adaptation through reducting

water use is not na option for the

many people already lacking

adequete access to safe water.

Access to water is subject to

various forms of discrimination,

for instance due to gender and

location. Poor and marginalized

water users are unable to

compete with water extraction by

industries, large-scale agriculture,

and other powerfull users.

Adaptation issues& prospects

Climatic Drivers TimeframeRisk & potential for

adaptation

47

Urban agglomerations by size class and potential risk of flooding

48

1970 2011

Urban agglomerations by size class and potential risk of flooding

49

Urban agglomerations by size class and potential risk of droughts

50

Urban agglomerations by size class and potential risk of droughts

1970 2011

51

World Urbanization Prospects, the 2011 RevisionUnited Nations, Department of Economic and Social Affairs, Population Division

Percentage of urban population and agglomerations by size class, 2011

Natural disasters related to climate (1995-2015)

Sub-título 24pt

Corpo 18pt

FONTE: CRED-UNISDR 2015

Population

Source of population grid: IBGE

Rural communities

Urban centers

Porto Velho, RO Manaus, AM

Santarém, PA Belém, PA