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Remote Linkages to Anomalous Winter Atmospheric Ridging over the Northeastern Pacific
Severe drought in California between 2013 and 2016 has been linked to the multiyear persistence of anomalously high atmospheric pressure over the northeastern Pacific Ocean, which deflected the Pacific storm track northward and suppressed regional precipitation during California's winter 'rainy season.' Multiple hypotheses have emerged regarding why this high pressure ridge near the west coast of North America was so resilient-including unusual sea surface temperature patterns in the Pacific Ocean, reductions in Arctic sea ice, random atmospheric variability, or some combination thereof. Here we explore relationships between previously documented atmospheric conditions over the North Pacific and several potential remote oceanic and cryospheric influences using both observational data and a large ensemble of climate model simulations. Our results suggest that persistent wintertime atmospheric ridging similar to that implicated in California's 2013-2016 drought can at least partially be linked to unusual Pacific sea surface temperatures, and that Pacific Ocean conditions may offer some degree of cool-season foresight in this region despite the presence of substantial internal variability.
A Framework for Evaluating Climate Model Performance Metrics
The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables?e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.
Investigating the linkage between simulated precipitation climatology and ENSO-related precipitation anomaly based on multi-model and perturbed parameter ensembles
It remains a challenge for climate models to correctly capture the relationship between precipitation and ENSO. This studyexamines the linkage between the simulated precipitation climatology and ENSO-related precipitation anomaly during borealwinter based on the multi-model ensemble from the Atmospheric Model Intercomparison Project Phase 5 (AMIP5) and perturbedparameter ensemble (PPE) with the Beijing Climate Center (BCC) atmospheric model. The AMIP5 models have large biases insimulating the tropical precipitation anomaly during El Niño, such as the shifts of the Inter-Tropical Convergence Zone (ITCZ)and South Pacific Convergence Zone (SPCZ). The inter-model differences show that the precipitation change in response to seasurface temperature (SST) change increases with enhanced precipitation climatology. The ENSO-related precipitation anomalycan also be related to the spatial distribution of the mean-state precipitation. The simulated ITCZ/SPCZ displacements aresignificantly correlated with the spatial precipitation-SST relationship in the mean state. Models with stronger mean-stateprecipitation-SST relationship also produce stronger SPCZ/ITCZ displacements. The mean-state tropical precipitation also hasstrong impacts on the ENSO-related precipitation anomalies over East Asia. In the BCC PPE, the connection between the mean-state precipitation and ENSO-related precipitation anomaly is overall consistent with that in AMIP5. Parameters associated withthe low-cloud and deep convection processes are the most influential ones for the precipitation simulations in BCC. Comparedwith the version in AMIP5, the new BCC model can better simulate the precipitation climatology and the relationship betweenENSO and precipitation over southern China. These results have important implications for model development and model-basedclimate predictions.
The Effect of Future Ambient Air Pollution on Human Premature Mortality to 2100 Using Output from the ACCMIP Model Ensemble
Ambient air pollution from ground-level ozone and fine particulate matter (PM(sub 2.5)) is associated with premature mortality. Future concentrations of these air pollutants will be driven by natural and anthropogenic emissions and by climate change. Using anthropogenic and biomass burning emissions projected in the four Representative Concentration Pathway scenarios (RCPs), the ACCMIP ensemble of chemistry climate models simulated future concentrations of ozone and PM(sub 2.5) at selected decades between 2000 and 2100. We use output from the ACCMIP ensemble, together with projections of future population and baseline mortality rates, to quantify the human premature mortality impacts of future ambient air pollution. Future air-pollution-related premature mortality in 2030, 2050 and 2100 is estimated for each scenario and for each model using a health impact function based on changes in concentrations of ozone and PM(sub 2.5) relative to 2000 and projected future population and baseline mortality rates. Additionally, the global mortality burden of ozone and PM(sub 2.5) in 2000 and each future period is estimated relative to 1850 concentrations, using present-day and future population and baseline mortality rates. The change in future ozone concentrations relative to 2000 is associated with excess global premature mortality in some scenarios/periods, particularly in RCP8.5 in 2100 (316 thousand deaths per year), likely driven by the large increase in methane emissions and by the net effect of climate change projected in this scenario, but it leads to considerable avoided premature mortality for the three other RCPs. However, the global mortality burden of ozone markedly increases from 382000 (121000 to 728000) deaths per year in 2000 to between 1.09 and 2.36 million deaths per year in 2100, across RCPs, mostly due to the effect of increases in population and baseline mortality rates. PM(sub 2.5) concentrations decrease relative to 2000 in all scenarios, due to projected reductions in emissions, and are associated with avoided premature mortality, particularly in 2100: between 2.39 and 1.31 million deaths per year for the four RCPs. The global mortality burden of PM(sub 2.5) is estimated to decrease from 1.70 (1.30 to 2.10) million deaths per year in 2000 to between 0.95 and 1.55 million deaths per year in 2100 for the four RCPs due to the combined effect of decreases in PM(sub 2.5) concentrations and changes in population and baseline mortality rates. Trends in future air-pollution-related mortality vary regionally across scenarios, reflecting assumptions for economic growth and air pollution control specific to each RCP and region. Mortality estimates differ among chemistry climate models due to differences in simulated pollutant concentrations, which is the greatest contributor to overall mortality uncertainty for most cases assessed here, supporting the use of model ensembles to characterize uncertainty. Increases in exposed population and baseline mortality rates of respiratory diseases magnify the impact on premature mortality of changes in future air pollutant concentrations and explain why the future global mortality burden of air pollution can exceed the current burden, even where air pollutant concentrations decrease.
Climate models show colorado drying sooner and with greater certainty east of the continental divide
Many studies have examined the aridity of the Colorado River Basin and the possible impacts of climate change which could further strain already over-allocated water resources in the region. Fewer studies have examined the multiple Colorado Rocky Mountain headwater regions specifically. This is especially true of areas East of the Continental Divide, despite water originating there being critical to cities and agriculture in Eastern Colorado and further downstream. This paper explores and compares drying trends in the Eastern and Western Colorado Rocky Mountains using single-model initial-condition large ensembles from ten global climate models. The use of multiple models allows us to identify signals that are consistent across different physics parameterizations, model grids, and other model intricacies. The large ensembles also allow us to quantify the time of emergence of these climate change signals--that is, when did (or when will) the long term change due to anthropogenic greenhouse gasses exceed the internal variability of the climate system. Consistent with previous studies, we find evidence of drying on both sides of the Continental Divide. That drying is more pronounced, occurs sooner, and is more consistent across global climate models in the East, however, highlighting the region’s importance despite generally receiving less attention than the West.
Pyrocumulonimbus Events Over British Columbia in 2017: An Ensemble Model Study of Parameter Sensitivities and Climate Impacts of Wildfire Smoke in the Stratosphere
Abstract The Pyrocumulonimbus (pyroCb) events over British Columbia in 2017 were observed in the lower stratosphere for about 8–10 months after the smoke injections. Several previous studies used global climate models to investigate the physical parameters for the 2017 pyroCb events, but the conclusions show strong model dependency. In this study, we use the Energy Exascale Earth System Model (E3SM) and complete an ensemble of runs exploring three injection parameters: smoke aerosol mass, the percentage of black carbon within the smoke aerosols, and plume injection height. Additionally, we consider the heterogeneous reaction of ozone and primary organic matter. According to the satellite daily observed aerosol optical depth (AOD), we find that the best ensemble member is the simulation with 0.4 Tg of smoke, 3% of which is black carbon, a 13.5 km smoke injection height, and a 10 −5 probability factor of the heterogeneous reaction. Besides AOD, we examine the ensemble score based on the metrics used in previous studies: the metrics of the extinction coefficient at 18 km altitude and the maximum plume height. The conclusion of the best estimate of the injection parameters for 2017 pyroCb events shows strong not only model but also evaluation metric dependency. We use the Random Forest machine learning technique to quantify the relative importance of each parameter in accurately simulating the 2017 pyroCb events and find that the injection height is the most critical feature, no matter which metric is used to score the ensemble members.
Atmospheric Solar Heating in Minor Absorption Bands
Solar radiation is the primary source of energy driving atmospheric and oceanic circulations. Concerned with the huge computing time required for computing radiative transfer in weather and climate models, solar heating in minor absorption bands has often been neglected. The individual contributions of these minor bands to the atmospheric heating is small, but collectively they are not negligible. The solar heating in minor bands includes the absorption due to water vapor in the photosynthetically active radiation (PAR) spectral region from 14284/cm to 25000/cm, the ozone absorption and Rayleigh scattering in the near infrared, as well as the O2 and CO2 absorption in a number of weak bands. Detailed high spectral- and angular-resolution calculations show that the total effect of these minor absorption is to enhance the atmospheric solar heating by approximately 10%. Depending upon the strength of the absorption and the overlapping among gaseous absorption, different approaches are applied to parameterize these minor absorption. The parameterizations are accurate and require little extra time for computing radiative fluxes. They have been efficiently implemented in the various atmospheric models at NASA/Goddard Space Flight Center, including cloud ensemble, mesoscale, and climate models.
Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States
Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.
Ensemble modeling of global lake evaporation under climate change
Global projections of lake evaporation are typically based on simulations using single mechanistic models. However, because of its complex interactions with various lake physical properties, environmental and anthropogenic drivers, lake evaporation is highly variable and sensitive to the choice of model used. In this study, we present a multi-model analysis to investigate differences across global simulations of lake evaporation during the warm-season using three different lake models driven by outputs from four general circulation models (GCM) (i.e. 12 model combinations in total) for historic and future scenarios. Our results suggest substantial differences among lake-climate model simulations of lake evaporation. These differences varied throughout the 20th and 21st century, with model driver data explaining 74% of the variance in future projections of warm- season lake evaporation. Our projections indicate that, by the end of the 21st century (2070–2099), global annual lake evaporation rates will increase by 10–27% under Representative Concentration Pathways (RCPs) 2.6–8.5. Finally, we highlight the importance of using a multi-model approach for the prediction of future global lake evaporation responses to climate change.
Assessing climate-change-induced flood risk in the Conasauga River watershed: an application of ensemble hydrodynamic inundation modeling
Abstract. This study evaluates the impact of potential future climate change on flood regimes, floodplain protection, and electricity infrastructures across the Conasauga River watershed in the southeastern United States through ensemble hydrodynamic inundation modeling. The ensemble streamflow scenarios were simulated by the Distributed Hydrology Soil Vegetation Model (DHSVM) driven by (1) 1981–2012 Daymet meteorological observations and (2) 11 sets of downscaled global climate models (GCMs) during the 1966–2005 historical and 2011–2050 future periods. Surface inundation was simulated using a GPU-accelerated Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) hydrodynamic model. A total of 9 out of the 11 GCMs exhibit an increase in the mean ensemble flood inundation areas. Moreover, at the 1 % annual exceedance probability level, the flood inundation frequency curves indicate a ∼ 16 km2 increase in floodplain area. The assessment also shows that even after flood-proofing, four of the substations could still be affected in the projected future period. The increase in floodplain area and substation vulnerability highlights the need to account for climate change in floodplain management. Overall, this study provides a proof-of-concept demonstration of how the computationally intensive hydrodynamic inundation modeling can be used to enhance flood frequency maps and vulnerability assessment under the changing climatic conditions.
Noise-induced transitions of the Atlantic Meridional Overturning Circulation in CMIP5 models
By studying transition probabilities of the Atlantic Meridional Overturning Circulation (AMOC) in an ensemble of CMIP5 climate models, we revisit one of the stability indicators of the AMOC, i.e. the freshwater transport carried by the AMOC at the southern boundary of the Atlantic basin. A correction to this indicator, based on the transition probabilities, is suggested to measure whether an AMOC state is in a multiple equilibrium regime or not. As a consequence, the AMOC of all CMIP5 models considered is in a multiple equilibrium regime and hence, in principle, a collapsed AMOC state should exist in each of these models. The results further demonstrate the dependence of the Atlantic surface freshwater flux on the AMOC and the impact of extreme events in the AMOC on temperatures in the North Atlantic region.
Climate Change Impacts on the South American Monsoon System and Its Surface–Atmosphere Processes Through RegCM4 CORDEX-CORE Projections
Here, we evaluate projected changes in surface water and energy balances and surface–atmosphere coupling in the South American Monsoon System (SAMS) for the end of the century (2080–2099). The analyses are based on two ensemble datasets, which follow Representative Concentration Pathway 8.5 in the future period, and cover four subdomains (Northern and Southern—NAMZ and SAMZ—Amazon, La Plata Basin—LPB, and Southern Southeast Brazil—SSB). One ensemble consists of three Global Climate Models (HadGEM2-ES, MPI-ESM-MR and NorESM1-M), while the other consists of their dynamically downscaled version at 25 km horizontal grid spacing using Regional Climate Model version 4 (RegCM4). As both ensembles are able in reproducing the annual cycle of the components of the surface water and energy balances in the present climate, they can be used in the study of future climate. During the wet season (November–March), both ensembles project a decrease in precipitation over NAMZ and SAMZ (an exception is RegCM4 that projects a slight increase in SAMZ), and an increase across the LPB and SSB. These changes do not cause retreat or expansion of the monsoon area over the continent, which is similar to the present climate (1995–2014). For the wet season, the ensembles are in line with the presence of a strong surface–atmosphere coupling in LPB and SSB, weak coupling in SAMZ and very weak coupling in NAMZ. For future climate, the coupling is even weaker in NAMZ, which may be a driver for the negative changes in precipitation. For the other subdomains, while the ensembles project similar signals of precipitation changes, they disagree with the surface-atmosphere coupling highlighting the uncertainties in future climate.
On the Role of Trend and Variability of Hydroxyl Radical (OH) in the Global Methane Budget
Decadal trends and interannual variations in the hydroxyl radical (OH), while poorly constrained at present, are critical for understanding the observed evolution of atmospheric methane (CH4). Through analyzing the OH fields simulated by the model ensemble of the Chemistry-Climate Model Initiative (CCMI), we find (1) the negative OH anomalies during the El Niño years mainly corresponding to the enhanced carbon monoxide (CO) emissions from biomass burning and (2) a positive OH trend during 1980-2010 dominated by the elevated primary production and the reduced loss of OH due to decreasing CO after 2000. Both two-box model inversions and variational 4D inversions suggest that ignoring the negative anomaly of OH during the El Niño years leads to a large overestimation of the increase in global CH4 emissions by up to10±3Tg yr-1to match the observed CH4 increase over these years. Not accounting for the increasing OH trends given by the CCMI models leads to an underestimation of the CH4 emission increase by 23±9Tg yr-1from 1986 to 2010. The variational inversion estimated CH4 emissions show that the tropical regions contribute most to the uncertainties related to OH. This study highlights the significant impact of climate and chemical feedbacks related to OH on the top-down estimates of the global CH4 budget.
Evaluating Diurnal and Semi-Diurnal Cycle of Precipitation in CMIP6 Models Using Satellite- and Ground-Based Observations
We report the diurnal and semi-diurnal cycle of precipitation simulated from CMIP6 models during 1996-2005 are evaluated globally between 60°S and 60°N, as well as at ten selected locations representing three categories of diurnal cycle of precipitation: (1) afternoon precipitation over land, (2) early morning precipitation over ocean, and (3) nocturnal precipitation over land. Three satellite-based and two ground-based rainfall products are used to evaluate the climate models. Globally, the ensemble mean of CMIP6 models shows a diurnal phase of 3 to 4 hours earlier over land and 1 to 2 hours earlier over ocean, when compared with the latest satellite products. These biases are in line with what were found in previous versions of climate models but reduced compared to the CMIP5 ensemble mean. Analysis at the selected locations complimented with in-situ measurements further reinforces these results. Several CMIP6 models have shown a significant improvement in the diurnal cycle of precipitation compared to their CMIP5 counterparts, notably on delaying afternoon precipitation over land. This can be attributed to the use of more sophisticated convective parameterizations. Most models are still unable to capture the nocturnal peak associated with elevated convection and propagating mesoscale convective systems, with a few exceptions that allow convection to be initiated above the boundary layer to capture nocturnal elevated convection. We also quantify an encouraging consistency between the satellite- and ground-based precipitation measurements despite differing spatiotemporal resolutions and sampling periods, which provides confidence in using them to evaluate the diurnal and semi-diurnal cycle of precipitation in climate models.
The Worldwide C3S CORDEX Grand Ensemble: A Major Contribution to Assess Regional Climate Change in the IPCC AR6 Atlas
The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of regional climate model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX domains with few archived simulations and performing quality control. It also addressed the lack of comprehensive documentation by compiling information from all contributing regional models, allowing for an informed use of data. In addition to presenting the worldwide CORDEX dataset, we assess here its consistency for precipitation and temperature by comparing climate change signals in regions with overlapping CORDEX domains, obtaining overall coincident regional climate change signals. The C3S CORDEX dataset has been used for the assessment of regional climate change in the IPCC AR6 (and for the interactive Atlas) and is available through the Copernicus Climate Data Store (CDS).
CMIP5 Climate Models Overestimate Cooling by Volcanic Aerosols
We compare projections of the observed hemispherical mean surface temperature (HadCRUT4.6.0.0) and the ensemble mean of CMIP5 climate models' simulations on a set of standard regression model forcing variables. We find that the volcanic aerosol regression coefficients of the CMIP5 simulations are consistently significantly larger (by 40–49%) than the volcanic aerosol coefficients of the observed temperature. The probability that the observed differences are caused just by chance is much less than 0.01. The overestimate is due to the climate models' response to volcanic aerosol radiative forcing. The largest overestimate occurs in the winter season of each hemisphere. We hypothesize that the models' parameterization of aerosol-cloud interactions within ice and mixed phase clouds is a likely source of this discrepancy. Furthermore, the models significantly underestimate the effect of solar variability on temperature for both hemispheres.
Importance of Internal Variability for Climate Model Assessment
Benchmarking climate model simulations against observations of the climate is core to the process of building realistic climate models and developing accurate future projections. However, in many cases, models do not match historical observations, particularly on regional scales. If there is a mismatch between modeled and observed climate features, should we necessarily conclude that our models are deficient? Using several illustrative examples, we emphasize that internal variability can easily lead to marked differences between the basic features of the model and observed climate, even when decades of model and observed data are available. This can appear as an apparent failure of models to capture regional trends or changes in global teleconnections, or simulation of extreme events. Despite a large body of literature on the impact of internal variability on climate, this acknowledgment has not yet penetrated many model evaluation activities, particularly for regional climate. We emphasize that using a single or small ensemble of simulations to conclude that a climate model is in error can lead to premature conclusions on model fidelity. A large ensemble of multidecadal simulations is therefore needed to properly sample internal climate variability in order to robustly identify model deficiencies and convincingly demonstrate progress between generations of climate models.