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At least 577 records · Page 32

Twenty‐First Century Drought Projections in the CMIP6 Forcing Scenarios

There is strong evidence climate change will increase drought risk and severity, but these conclusions depend on the regions, seasons, and drought metrics being considered. We analyze changes in drought across the hydrologic cycle (precipitation, soil moisture, and runoff) in projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6). The multi‐model ensemble shows robust drying in the mean state across many regions and metrics by the end of the 21st century, even following the more aggressive mitigation pathways (SSP1‐2.6 and SSP2‐4.5). Regional hotspots with strong drying include western North America, Central America, Europe and the Mediterranean, the Amazon, southern Africa, China, Southeast Asia, and Australia. Compared to SSP3‐7.0 and SSP5‐8.5, however, the severity of drying in the lower warming scenarios is substantially reduced and further precipitation declines in many regions are avoided. Along with drying in the mean state, the risk of the historically most extreme drought events also increases with warming, by 200–300% in some regions. Soil moisture and runoff drying in CMIP6 is more robust, spatially extensive, and severe than precipitation, indicating an important role for other temperature‐sensitive drought processes, including evapotranspiration and snow. Given the similarity in drought responses between CMIP5 and CMIP6, we speculate both generations of models are subject to similar uncertainties, including vegetation processes, model representations of precipitation, and the degree to which model responses to warming are consistent with observations. These topics should be further explored to evaluate whether CMIP6 models offer reasons to have increased confidence in drought projections.

drought↗

Larger Drought and Flood Hazards and Adverse Impacts on Population and Economic Productivity Under 2.0 than 1.5°C Warming

Climate change may have major influences on surface runoff, which would consequently result in important implications for terrestrial ecosystems and human well‐being. At global scale there is limited understanding of these issues with respect to the warming targets stipulated in the Paris Agreement. Here we use a well‐established hydrological model (Variable Infiltration Capacity [VIC]) forced with a representative ensemble of latest climate projections from four global circulation models (GCMs) to estimate potential future changes in runoff and Terrestrial Ecosystem Water Retention (TEWR), as well as changes in extreme runoff and their impacts on population, and overall gross domestic product (GDP) worldwide. Results suggest that annual runoff generally would have larger increases, while annual TEWR generally would have larger decreases under the 2.0°C warming scenario as opposed to 1.5°C warming scenario. Global mean warming of 2°C versus 1.5°C would lead to more distinct spatial patterns in runoff change, with a general shift of the runoff distribution towards more extreme low runoff in Mexico, western United States, Western Europe, southeastern China, West Siberian Plain and more extreme high runoff in Alaska, northern Canada, and large parts of Asia. More people and GDP would be exposed to extreme low runoff decrease, extreme high runoff increase, extreme low runoff decrease as well as extreme high runoff increase under a higher warming scenario. This study differentiates hydrological impacts between the two warming scenarios and illustrates higher runoff, lower TEWR, larger potential drought and flood hazards and adverse impacts on population and GDP under 2°C than 1.5°C.

Paris Agreement↗

Toward a Consistent Definition between Satellite and Model Clear-Sky Radiative Fluxes

A new method of determining clear-sky radiative fluxes from satellite observations for climate model evaluation is presented. The method consists of applying adjustment factors to existing satellite clear-sky broadband radiative fluxes that make the observed and simulated clear-sky flux definitions more consistent. The adjustment factors are determined from the difference between observation-based radiative transfer model calculations of monthly mean clear-sky fluxes obtained by ignoring clouds in the atmospheric column and by weighting hourly mean clear-sky fluxes with imager-based clear-area fractions. The global mean longwave (LW) adjustment factor is −2.2 W m−2 at the top of the atmosphere and 2.7 W m−2 at the surface. The LW adjustment factors are pronounced at high latitudes during winter and in regions with high upper-tropospheric humidity and cirrus cloud cover, such as over the west tropical Pacific, and the South Pacific and intertropical convergence zones. In the shortwave (SW), global mean adjustment is 0.5 W m−2 at TOA and −1.9 W m−2 at the surface. It is most pronounced over sea ice off of Antarctica and over heavy aerosol regions, such as eastern China. However, interannual variations in the regional SW and LW adjustment factors are small compared to those in cloud radiative effect. After applying the LW adjustment factors, differences in zonal mean cloud radiative effect between observations and climate models decrease markedly between 60°S and 60°N and poleward of 65°N. The largest regional improvements occur over the west tropical Pacific and Indian Oceans. In contrast, the impact of the SW adjustment factors is much smaller.

Norman G Loeb↗

Historical (1850‐2014) Aerosol Evolution and Role on Climate Forcing Using the GISS Modele2.1 Contribution to CMIP6

The Earth’s climate is rapidly changing. Over the past centuries, aerosols, via their ability to absorb or scatter solar radiation and alter clouds, played an important role in counterbalancing some of the greenhouse gas (GHG) caused global warming. The multi-century anthropogenic aerosol cooling effect prevented present-day climate from reaching even higher surface air temperatures and subsequent more dramatic climate impacts. Trends in aerosol concentrations and optical depth show that in many polluted regions such as Europe and the United States of America, aerosol precursor emissions decreased back to levels of the 1950s. More recent polluting countries such as China may have reached a turning point in recent years as well, while India still follows an upward trend. Here we study aerosol trends in the CMIP6 simulations of the GISS ModelE2.1 climate model using a fully coupled atmosphere composition onfiguration, including interactive gas-phase chemistry, and either an aerosol microphysical (MATRIX) or a mass-based (OMA) aerosol module. Results show that whether global aerosol radiative forcing is already declining depends on the aerosol scheme used. Using the aerosol microphysical scheme, where the aerosol system reacts more strongly to the trend in sulfur dioxide (SO2) emissions, global peak direct aerosol forcing was reached in the 1980’s, whereas the mass-based scheme simulates peak direct aerosol forcing around 2010.

Aerosol forcing↗

Influence of Cloud, Fog, and High Relative Humidity during Pollution Transport Events in South Korea: Aerosol Properties and PM2.5 Variability

This investigation examines aerosol dynamics during major fine mode aerosol transboundary pollution events in South Korea primarily during the KORUS-AQ campaign from May 1 – June 10, 2016, particularly when cloud fraction was high and/or fog was present to quantify the change in aerosol characteristics due to near-cloud or fog interaction. We analyze the new AERONET Version 3 data that have significant changes to cloud screening algorithms, allowing many more fine-mode observations in the near vicinity of clouds or fog. Case studies for detailed investigation include May 25–26, 2016 when cloud fraction was high over much of the peninsula, associated with a weak frontal passage and advection of pollution from China. These cloud-influenced Chinese transport dates also had the highest aerosol optical depth (AOD), surface PM2.5 concentrations and fine mode particle sizes of the entire campaign. Another likewise cloud/high relative humidity (RH) case is June 9 and 10, 2016 when fog was present over the Yellow Sea that appears to have affected aerosol properties well downwind over the Korean peninsula. In comparison we also investigated aerosol properties on air stagnation days with very low cloud cover and relatively low RH (May 17 & 18, 2016), when local Korean emissions dominated. Aerosol volume size distributions show marked differences between the transport days (with high RH and cloud influences) and the local pollution stagnation days, with total column-integrated particle fine mode volume being an order of magnitude greater on the pollution transport dates. The PM2.5 over central Seoul were significantly greater than for coastal sites on the transboundary transport days yet not on stagnation days, suggesting addi-tional particle formation from gaseous urban emissions in cloud/fog droplets and/or in the high RH humidified aerosol environment. Many days had KORUS-AQ research aircraft flights that provided observations of aerosol absorption, particle chemistry and vertical profiles of extinction. AERONET retrievals and aircraft in situ mea-surements both showed high single scattering albedo (weak absorption) on the cloudy or cloud influenced days, plus aircraft profile in situ measurements showed large AOD enhancements (versus dried aerosol) at ambient relative humidity (RH) on the pollution transport days, consistent with the significantly larger fine mode particle radii and weak absorption.

Aerosol↗

Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)

Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998–2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R2 = 0.90–0.92; slope = 0.90–0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (−0.28 ± 0.03 μg/m3/yr), Europe (−0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005–2013 and the negative trend (−3.37 ± 0.38 μg/m3/yr) for China over 2011–2018 are remarkable, with implications for the health of billions of people.

Melanie S. Hammer↗

Response of Surface Shortwave Cloud Radiative Effect to Greenhouse Gases and Aerosols and Its Impact on Summer Maximum Temperature

Shortwave cloud radiative effects (SWCREs), defined as the difference of the shortwave radiative flux between all-sky and clear-sky conditions at the surface, have been reported to play an important role in influencing the Earth's energy budget and temperature extremes. In this study, we employed a set of global climate models to examine the SWCRE responses to CO2, black carbon (BC) aerosols, and sulfate aerosols in boreal summer over the Northern Hemisphere. We found that CO2 causes positive SWCRE changes over most of the NH, and BC causes similar positive responses over North America, Europe, and eastern China but negative SWCRE over India and tropical Africa. When normalized by effective radiative forcing, the SWCRE from BC is roughly 3–5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from changes in relative humidity (RH) and, to a lesser extent, changes in cloud liquid water, circulation, dynamics, and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that for CO2 and BC because part of the radiation scattered by clouds under all-sky conditions will also be scattered by aerosols under clear-sky conditions. Using a multilinear regression model, it is found that mean daily maximum temperature (Tmax) increases by 0.15 and 0.13 K per watt per square meter (W m−2) increase in local SWCRE under the CO2 and BC experiment, respectively. When domain-averaged, the contribution of SWCRE change to summer mean Tmax changes was 10 %–30 % under CO2 forcing and 30 %–50 % under BC forcing, varying by region, which can have important implications for extreme climatic events and socioeconomic activities.

surface shortwave cloud radiative effect↗

Impact of COVID-Global Shutdown on Earth's Radiation Budget

Climate is fueled by how much of the sun’s energy is absorbed by Earth. Changes in aerosol emissions have a direct impact how much solar energy is absorbed under clear-sky conditions, which occurs roughly one-third of the time globally. Aerosol emission changes can also indirectly influence clouds and further alter solar energy absorption. In this presentation, we examine the impact of the COVID-19 global shutdown on Earth’s radiation budget at global and regional scales and compare it with other changes that have had an influence on Earth's radiation budget during the past 20 years—including policy-related emission controls, wildfire events and changes in sea-ice extent in polar regions. We use a novel analysis technique together with data from NASA instruments on the Terra, Aqua and Aura satellites and reanalysis data from MERRA-2. We also compare results with state-of-the-art general circulation models that have been nudged to reanalysis data (winds, air temperature and surface 25 pressure) at all levels. Our preliminary results show that the largest COVID-19 impact occurs regionally over the East Asian Marginal Seas, just off the coast of China. We find a 30% reduction in aerosol optical depth according to the MODIS instrument, and an 8% reduction in reflected solar radiation according to the CERES radiation budget instrument. The climate model simulations show remarkable consistency with the observations and furthermore show no evidence of an indirect effect on cloud properties.

Norman G. Loeb↗

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Jianxiu Qiu↗

Understanding The Top-Of-Atmosphere Fluxes Difference Between Aerocom Phase III Models And The CERES Product: Clear-Sky Perspective

The Clouds and the Earth’s Radiant Energy System (CERES) project produces a long-term global climate data record (CDR) that can be used to detect decadal changes in the Earth’s radiation budget (ERB) from the surface to the top-of-atmosphere (TOA). The CERES Energy Balanced and Filled (EBAF) product includes monthly mean shortwave (SW), longwave (LW), and net TOA all-sky and clear-sky radiative fluxes over 1-degree latitude by 1-degree longitude regions. The EBAF SW and LW fluxes are adjusted within their uncertainties to be consistent with the heat storage in the Earth-atmosphere system (Johnson et al. 2016). EBAF also provides a gap-free monthly mean clear-sky flux map by inferring clear-sky fluxes from both CERES and MODIS measurements (Loeb et al. 2018). In this study, we compare the TOA clear-sky fluxes from Aerocom phase III output with those from the CERES EBAF products. Flux differences over the ocean are generally smaller than over the land, and the magnitude of the differences shows seasonal and regional dependency. To understand the flux differences, aerosol optical depths (AOD) from the Aerocom models are compared with the satellite retrievals from MODIS and MISR. Over the ocean, the AOD differences and the flux differences show consistent regional features, indicating that the differences between models and observations are robust as the CERES EBAF clear-sky SW fluxes and MODIS/MISR AODs are determined independently. However, very little resemblance is found between the AOD and flux differences over the land. To further understand the cause of the flux differences over the land, we compare the land surface albedo from MODIS with the albedo from the models and find consistency in regional albedo differences and flux differences. Monthly regional radiative kernels of AOD and surface albedo derived using the MERRA-2 reanalysis data (Thorsen et al. 2020) are applied to AOD and surface albedo differences between models and observations. For most of the models, the AOD and surface albedo differences can explain most of the flux differences between models and CERES EBAF. The EOS era satellites have provided 20 years of carefully calibrated and validated observations that are suitable for trend analysis. Both the MODIS AOD and CERES EBAF clear-sky SW flux show decreasing trends over the coastal regions of eastern China and the eastern United States due to the emission control policies enforced in both countries, and an increasing trend off the coast of India. The AOD and clear-sky SW flux trends are less consistent over land, as the surface albedo changes complicate the clear-sky SW flux trend. The AOD and clear-sky SW flux trends from the Oslo model HIST run are also examined. However, none of the aforementioned regional trends are found in the model results.

Wenying Su↗

Reduction of Spectral Radiance Reflectance During the Annular Solar Eclipse of 21 June 2020 from DSCOVR/EPIC

We have analyzed three EPIC images during the annular solar eclipse on June 21, 2020 when centers of the eclipse were in the Arabian Peninsula (mostly desert), the Himalayas (mostly barren land), and China (mostly cloudy over vegetation) and compared with two images for 2017 American solar eclipse over Casper, WY and Columbia, MO (vegetated surface for both). We found that the global average reductions of spectral reflectance for the three images during 2020 solar eclipse are quite different while the reductions of spectral reflectance for the two images for 2017 solar eclipse are similar. Radiative transfer model simulations suggest that different surface spectral albedo and cloud fraction attribute to the different reduction of spectral reflectance for three images during 2020 eclipse while similar spectral albedo of vegetated surface around Casper and Columbia are the main cause for the similar spectral reflectance reduction for the two images during 2017 solar eclipse.

solar eclipse↗

Convective Transport and Evolution of Clouds and aerosols in the Asian Summer Monsoon Region

The existence of a persistent layer in the lowermost stratosphere in the Asian Monsoon region is well-known and has been observed by satellites and also in situ by balloon-borne sensors. This aerosol layer has been observed to persist every year by both SAGE sensors and by CALIOP, between the tropopause and the lower boundary of the Junge stratospheric sulfate layer. But how does this layer form and why? Apparently, aerosols are lofted by convection form the Indian and Asian continents, into the upper troposphere/lower stratosphere (UT/LS) area in the middle of a persistent anticyclone associate with the Asian summer monsoon. But some mysteries remain. The top of this aerosol layer is well above the altitude of maximum convective outflow, so why is the top of the aerosol layer so high? Are the aerosols coming from China or from India? And finally, is there an observable impact of these aerosol on thin cirrus distributions in the UT/LS? We explore the interesting difference in altitude between the ATAL layer and the convective cloud top height, using observations from CALIOP and from SAGE. In the process of doing this we examine the accuracy of both CALIOP and SAGE cloud/aerosol discrimination, and the impact of various averaging lengths. We compare the apparent "observational tropopause" with observations from COSMIC, CALIOP (clouds), and with modeled interpolation from MERRA-2. We also compare CALIOP and CloudSat daytime cloud top heights for convective clouds with the vertical aerosol distribution, and evaluate convection in this region as represented by MERRA-2. Finally, and maybe most importantly we compare convective cloud top heights and temperatures with diurnal variability during the most recent years by using observations from the geostationary Himawari satellite. How representative are the twice-daily A-Train observations, when compared to the natural diurnal variability of convection? The goal is to establish the origins of these UT/LS aerosols, whether these aerosol distributions can be completely explained by convective transport, and to evaluate convective lofting of aerosol by models. Ultimately, the convective lofting of aerosols as modeled is needed to explain the Asian Monsoon aerosol distributions. The observations contribute much-needed information to enhance the model accuracy.

clouds↗

A data–model approach to interpreting speleothem oxygen isotope records from monsoon regions

Reconstruction of past changes in monsoon climate from speleothem oxygen isotope (δ 18 O) records is complex because δ 18 O signals can be influenced by multiple factors including changes in precipitation, precipitation recycling over land, temperature at the moisture source, and changes in the moisture source region and transport pathway. Here, we analyse >150 speleothem records of the Speleothem Isotopes Synthesis and AnaLysis (SISAL) database to produce composite regional trends in δ 18 O in monsoon regions; compositing minimises the influence of site-specific karst and cave processes that can influence individual site records. We compare speleothem δ 18 O observations with isotope-enabled climate model simulations to investigate the specific climatic factors causing these regional trends. We focus on differences in δ 18 O signals between the mid-Holocene, the peak of the Last Interglacial (Marine Isotope Stage 5e) and the Last Glacial Maximum as well as on δ 18 O evolution through the Holocene. Differences in speleothem δ 18 O between the mid-Holocene and the Last Interglacial in the East Asian and Indian monsoons are small, despite the larger summer insolation values during the Last Interglacial. Last Glacial Maximum δ 18 O values are significantly less negative than interglacial values. Comparison with simulated glacial–interglacial δ 18 O shows that changes are principally driven by global shifts in temperature and regional precipitation. Holocene speleothem δ 18 O records show distinct and coherent regional trends. Trends are similar to summer insolation in India, China and southwestern South America, but they are different in the Indonesian–Australian region. Redundancy analysis shows that 37 % of Holocene variability can be accounted for by latitude and longitude, supporting the differentiation of records into individual monsoon regions. Regression analysis of simulated precipitation δ 18 O and climate variables show significant relationships between global Holocene monsoon δ 18 O trends and changes in precipitation, atmospheric circulation and (to a lesser extent) source area temperature, whereas precipitation recycling is non-significant. However, there are differences in regional-scale mechanisms: there are clear relationships between changes in precipitation and δ 18 O for India, southwestern South America and the Indonesian–Australian regions but not for the East Asian monsoon. Changes in atmospheric circulation contribute to δ 18 O trends in the East Asian, Indian and Indonesian–Australian monsoons, and a weak source area temperature effect is observed over southern and central America and Asia. Precipitation recycling is influential in southwestern South America and southern Africa. Overall, our analyses show that it is possible to differentiate the impacts of specific climatic mechanisms influencing precipitation δ 18 O and use this analysis to interpret changes in speleothem δ 18 O.

speleothem oxygen isotope records↗

Global Impact of COVID-19 Restrictions on the Atmospheric Concentrations of Nitrogen Dioxide and Ozone

Social-distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business-as-usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO2) and ozone (O3) at 5,756 observation sites in countries from January through June 2020. Reductions in NO2 coincide with timing and intensity of COVID-19 restrictions, ranging from 60% in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO2 concentrations were (13-23) % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5% to the business-as-usual estimate. NO2 reductions in Europe and the US have been more gradual with a halting recovery starting in late March. We estimate that the global NOx (NO+NO2) emission reduction during the first 6 months of 2020 amounted to 3.1 (2.6-3.6) TgN, equivalent to 5.5(4.7-6.4) % of the annual anthropogenic total. The response of surface O3 is complicated by competing influences of non-linear atmospheric chemistry. While surface O3 increased by up to 50% in some locations, we find the overall net impact on daily average O3 between February -June 2020 to be small. However, our analysis indicates a flattening of the O3 diurnal cycle with an increase in nighttime ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production.

Christoph A Keller↗

Global Carbon Budget 2019

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E(FF)) are based on energy statistics and cement production data, while emissions from land use change (E(LUC)), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S(OCEAN)) and terrestrial CO2 sink (S(LAND)) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B(IM)), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), E(FF) was 9.5±0.5 GtC/yr, E(LUC) 1.5±0.7 GtC/yr, G(ATM) 4.9±0.02 GtC/yr (2.3±0.01 ppm/yr), S(OCEAN) 2.5±0.6 GtC/yr, and S(LAND) 3.2±0.6 GtC/yr, with a budget imbalance B(IM) of 0.4 GtC/yr indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E(FF) was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC/yr, reaching 10 GtC/yr for the first time in history, E(LUC) was 1.5±0.7 GtC/yr, for total anthropogenic CO2 emissions of 11.5±0.9 GtC/yr (42.5±3.3 GtCO2). Also for 2018, G(ATM) was 5.1±0.2 GtC/yr (2.4±0.1 ppm/yr), S(OCEAN) was 2.6±0.6 GtC/yr, and S(LAND) was 3.5±0.7 GtC/yr, with a B(IM) of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in E(FF) of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC/yr persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013).

global carbon budget 2019↗

Uncertainty Quantification of Global Net Methane Emissions From Terrestrial Ecosystems Using a Mechanistically Based Biogeochemistry Model

Quantification of methane (CH4) emissions from wetlands and its sinks from uplands is still fraught with large uncertainties. Here, a methane biogeochemistry model was revised, parameterized, and verified for various wetland ecosystems across the globe. The model was then extrapolated to the global scale to quantify the uncertainty induced from four different types of uncertainty sources including parameterization, wetland type distribution, wetland area distribution, and meteorological input. We found that global wetland emissions are 212 ± 62 and 212 ± 32 Tg CH4 year−1 (1Tg = 1012 g) due to uncertain parameters and wetland type distribution, respectively, during 2000–2012. Using two wetland distribution data sets and three sets of climate data, the model simulations indicated that the global wetland emissions range from 186 to 212 CH4 year−1 for the same period. The parameters were the most significant uncertainty source. After combining the global methane consumption in the range of −34 to −46 Tg CH4 year−1, we estimated that the global net land methane emissions are 149–176 Tg CH4 year−1 due to uncertain wetland distribution and meteorological input. Spatially, the northeast United States and Amazon were two hotspots of methane emission, while consumption hotspots were in the Eastern United States and eastern China. During 1950–2016, both wetland emissions and upland consumption increased during El Niño events and decreased during La Niña events. This study highlights the need for more in situ methane flux data, more accurate wetland type, and area distribution information to better constrain the model uncertainty.

wetland methane emission↗

Decadal Changes in the Interannual Variability of Heat Waves in East Asia Caused by Atmospheric Teleconnection Changes

The heat wave in East Asia is examined by using empirical orthogonal function analysis to isolate dominant heat-wave patterns in the ground-based temperature observations over the Korean Peninsula and China and related large-scale atmospheric circulations obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research Reanalysis 1 during 1973–2012. This study focuses particularly on the interannual variability of heat waves and its decadal change. The analysis identifies two major atmospheric teleconnection patterns playing an important role in developing typical heat-wave patterns in East Asia—the Scandinavian (SCAND) and the circumglobal teleconnection (CGT) patterns, which exhibit a significant decadal change in the interannual variability in the mid-1990s. Before the mid-1990s, heat-wave occurrence was closely related to the CGT pattern, whereas the SCAND pattern is more crucial to explain heat-wave variability in the recent period. The stationary wave model experiments suggest an intensification of the SCAND pattern in the recent period driven by an increase in land–atmosphere interaction over Eurasia and decadal change in the dominant heat-wave patterns in East Asia.

heat wave patterns↗