95 results found
Thomas C, Voulgarakis A, Lim G, et al., 2021, An unsupervised learning approach to identifying blocking events: the case of European summer, Weather and Climate Dynamics, Vol: 2, Pages: 581-608, ISSN: 2698-4016
Atmospheric blocking events are mid-latitude weather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat waves in summer and cold snaps in winter. Despite being central features of mid-latitude synoptic-scale weather, there is no well-defined historical dataset of blocking events. Various blocking indices (BIs) have thus been suggested for automatically identifying blocking events in observational and in climate model data. However, BIs show significant regional and seasonal differences so that several indices are typically applied in combination to ensure scientific robustness. Here, we introduce a new BI using self-organizing maps (SOMs), an unsupervised machine learning approach, and compare its detection skill to some of the most widely applied BIs. To enable this intercomparison, we first create a new ground truth time series classification of European blocking based on expert judgement. We then demonstrate that our method (SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in the input data and reduces the dependence on arbitrary thresholds. Using ERA5 reanalysis data (1979–2019), we find that the SOM-BI identifies blocking events with a higher precision and recall than other BIs. In particular, SOM-BI already performs well using only around 20 years of training data so that observational records are long enough to train our new method. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups of SOM nodes can be an effective tool to diagnose such weather events, although the domain-based approach can still lead to errors in the identification of certain events in a fashion similar to the other BIs. We further test the red blocking detection skill of SOM-BI depending on the meteorological variable used to study blocking, including geopotential height, sea level pressure and four variables related to
Kuhn- Regnier A, Voulgarakis A, Nowack P, et al., 2021, The importance of antecedent vegetation and drought conditions as global drivers of burnt areas, Biogeosciences, Vol: 18, Pages: 3861-3879, ISSN: 1726-4170
The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.
Kuhn-Régnier A, Voulgarakis A, Nowack P, et al., 2021, Supplementary material to "Quantifying the Importance of Antecedent Fuel-Related VegetationProperties for Burnt Area using Random Forests", Biogeosciences, ISSN: 1726-4170
Qu Y, Voulgarakis A, Wang T, et al., 2021, A study of the effect of aerosols on surface ozone through meteorology feedbacks over China, ATMOSPHERIC CHEMISTRY AND PHYSICS, Vol: 21, Pages: 5705-5718, ISSN: 1680-7316
Hodnebrog Ø, Myhre G, Kramer RJ, et al., 2020, The effect of rapid adjustments to halocarbons and N2O on radiative forcing, npj Climate and Atmospheric Science, Vol: 3, Pages: 1-7, ISSN: 2397-3722
Rapid adjustments occur after initial perturbation of an external climate driver (e.g., CO2) and involve changes in, e.g. atmospheric temperature, water vapour and clouds, independent of sea surface temperature changes. Knowledge of such adjustments is necessary to estimate effective radiative forcing (ERF), a useful indicator of surface temperature change, and to understand global precipitation changes due to different drivers. Yet, rapid adjustments have not previously been analysed in any detail for certain compounds, including halocarbons and N2O. Here we use several global climate models combined with radiative kernel calculations to show that individual rapid adjustment terms due to CFC-11, CFC-12 and N2O are substantial, but that the resulting flux changes approximately cancel at the top-of-atmosphere due to compensating effects. Our results further indicate that radiative forcing (which includes stratospheric temperature adjustment) is a reasonable approximation for ERF. These CFCs lead to a larger increase in precipitation per kelvin surface temperature change (2.2 ± 0.3% K−1) compared to other well-mixed greenhouse gases (1.4 ± 0.3% K−1 for CO2). This is largely due to rapid upper tropospheric warming and cloud adjustments, which lead to enhanced atmospheric radiative cooling (and hence a precipitation increase) and partly compensate increased atmospheric radiative heating (i.e. which is associated with a precipitation decrease) from the instantaneous perturbation.
Mansfield L, Nowack P, Kasoar M, et al., 2020, Predicting global patterns of long-term climate change from short-term simulations using machine learning, npj Climate and Atmospheric Science, Vol: 3, ISSN: 2397-3722
Understanding and estimating regional climate change under different anthropogenic emission scenarios is pivotal for informing societal adaptation and mitigation measures. However, the high computational complexity of state-of-the-art climate models remains a central bottleneck in this endeavour. Here we introduce a machine learning approach, which utilises a unique dataset of existing climate model simulations to learn relationships between short-te¬rm and long-term temperature responses to different climate forcing scenarios. This approach not only has the potential to accelerate climate change projections by reducing the costs of scenario computations, but also helps uncover early indicators of modelled long-term climate responses, which is of relevance to climate change detection, predictability and attribution. Our results highlight challenges and opportunities for data-driven climate modelling, especially concerning the incorporation of even larger model datasets in the future. We therefore encourage extensive data sharing among research institutes to build ever more powerful climate response emulators, and thus to enable faster climate change projections.
Hantson S, Kelley DI, Arneth A, et al., 2020, Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project, Geoscientific Model Development, Vol: 13, Pages: 3299-3318, ISSN: 1991-959X
Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle and to infer relationships between climate, land use and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, in order to improve projections of fire characteristics and fire impacts on ecosystems and human societies in the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent interannual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the Northern Hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions, and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears
Tang T, Shindell D, Zhang Y, et al., 2020, Response of surface shortwave cloud radiative effect to greenhouse gases and aerosols and its impact on summer maximum temperature, Atmospheric Chemistry and Physics, Vol: 20, Pages: 8251-8266, ISSN: 1680-7316
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.
Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations).For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or
Wild O, Voulgarakis A, O'Connor F, et al., 2020, Global sensitivity analysis of chemistry-climate model budgets of tropospheric ozone and OH: exploring model diversity, Atmospheric Chemistry and Physics, Vol: 20, Pages: 4047-4058, ISSN: 1680-7316
Projections of future atmospheric composition change and its impacts on air quality and climate depend heavily on chemistry–climate models that allow us to investigate the effects of changing emissions and meteorology. These models are imperfect as they rely on our understanding of the chemical, physical and dynamical processes governing atmospheric composition, on the approximations needed to represent these numerically, and on the limitations of the observations required to constrain them. Model intercomparison studies show substantial diversity in results that reflect underlying uncertainties, but little progress has been made in explaining the causes of this or in identifying the weaknesses in process understanding or representation that could lead to improved models and to better scientific understanding. Global sensitivity analysis provides a valuable method of identifying and quantifying the main causes of diversity in current models. For the first time, we apply Gaussian process emulation with three independent global chemistry-transport models to quantify the sensitivity of ozone and hydroxyl radicals (OH) to important climate-relevant variables, poorly characterised processes and uncertain emissions. We show a clear sensitivity of tropospheric ozone to atmospheric humidity and precursor emissions which is similar for the models, but find large differences between models for methane lifetime, highlighting substantial differences in the sensitivity of OH to primary and secondary production. This approach allows us to identify key areas where model improvements are required while providing valuable new insight into the processes driving tropospheric composition change.
Quantifying the efficacy of different climate forcings is important for understanding the real‐world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near‐surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy‐induced bias of ±0.4 K in equilibrium climate sensitivity to CO2 doubling when estimated using the historical GSAT trend.
Nowack P, Ong QYE, Braesicke P, et al., 2019, Machine learning parameterizations for ozone: climate model transferability, https://sites.google.com/view/climateinformatics2019/proceedings, 9th International Workshop on Climate Informatics, Publisher: UCAR, Pages: 263-268
Many climate modeling studies have demon-strated the importance of two-way interactions betweenozone and atmospheric dynamics. However, atmosphericchemistry models needed for calculating changes in ozoneare computationally expensive. Nowack et al.  high-lighted the potential of machine learning-based ozoneparameterizations in constant climate forcing simulations,with ozone being predicted as a function of the atmo-spheric temperature state. Here we investigate the roleof additional time-lagged temperature information underpreindustrial forcing conditions. In particular, we testif the use of Long Short-Term Memory (LSTM) neuralnetworks can significantly improve the predictive skill ofthe parameterization. We then introduce a novel workflowto transfer the regression model to the new UK EarthSystem Model (UKESM). For this, we show for the firsttime how machine learning parameterizations could betransferred between climate models, a pivotal step tomaking any such parameterization widely applicable inclimate science. Our results imply that ozone parame-terizations could have much-extended scope as they arenot bound to individual climate models but, once trained,could be used in a number of different models. We hope tostimulate similar transferability tests regarding machinelearning parameterizations developed for other Earthsystem model components such as ocean eddy modeling,convection, clouds, or carbon cycle schemes.
Scannell C, Booth BBB, Dunstone NJ, et al., 2019, The influence of remote aerosol forcing from industrialized economies on the future evolution of East and West African rainfall, Journal of Climate, Vol: 32, Pages: 8335-8354, ISSN: 0894-8755
Past changes in global industrial aerosol emissions have played a significant role in historical shifts in African rainfall, and yet assessment of the impact on African rainfall of near-term (10–40 yr) potential aerosol emission pathways remains largely unexplored. While existing literature links future aerosol declines to a northward shift of Sahel rainfall, existing climate projections rely on RCP scenarios that do not explore the range of air quality drivers. Here we present projections from two emission scenarios that better envelop the range of potential aerosol emissions. More aggressive emission cuts result in northward shifts of the tropical rainbands whose signal can emerge from expected internal variability on short, 10–20-yr time horizons. We also show for the first time that this northward shift also impacts East Africa, with evidence of delays to both onset and withdrawal of the short rains. However, comparisons of rainfall impacts across models suggest that only certain aspects of both the West and East African model responses may be robust, given model uncertainties. This work motivates the need for wider exploration of air quality scenarios in the climate science community to assess the robustness of these projected changes and to provide evidence to underpin climate adaptation in Africa. In particular, revised estimates of emission impacts of legislated measures every 5–10 years would have a value in providing near-term climate adaptation information for African stakeholders.
Hodnebrog O, Myhre G, Samset BH, et al., 2019, Water vapour adjustments and responses differ between climate drivers, Atmospheric Chemistry and Physics, Vol: 19, Pages: 12887-12899, ISSN: 1680-7316
Water vapour in the atmosphere is the source of a major climate feedback mechanism and potential increases in the availability of water vapour could have important consequences for mean and extreme precipitation. Future precipitation changes further depend on how the hydrological cycle responds to different drivers of climate change, such as greenhouse gases and aerosols. Currently, neither the total anthropogenic influence on the hydrological cycle nor that from individual drivers is constrained sufficiently to make solid projections. We investigate how integrated water vapour (IWV) responds to different drivers of climate change. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulfate have been perturbed separately. While the global-mean IWV is usually assumed to increase by ∼7 % per kelvin of surface temperature change, we find that the feedback response of IWV differs somewhat between drivers. Fast responses, which include the initial radiative effect and rapid adjustments to an external forcing, amplify these differences. The resulting net changes in IWV range from 6.4±0.9 % K−1 for sulfate to 9.8±2 % K−1 for BC. We further calculate the relationship between global changes in IWV and precipitation, which can be characterized by quantifying changes in atmospheric water vapour lifetime. Global climate models simulate a substantial increase in the lifetime, from 8.2±0.5 to 9.9±0.7 d between 1986–2005 and 2081–2100 under a high-emission scenario, and we discuss to what extent the water vapour lifetime provides additional information compared to analysis of IWV and precipitation separately. We conclude that water vapour lifetime changes are an important indicator of changes in precipitation patterns and that BC is particularly efficient in prolonging the mean time, and therefore like
Sillmann J, Stjern CW, Myhre G, et al., 2019, Extreme wet and dry conditions affected differently by greenhouse gases and aerosols, npj Climate and Atmospheric Science, Vol: 2, Pages: 1-7, ISSN: 2397-3722
Global warming due to greenhouse gases and atmospheric aerosols alter precipitation rates, but the influence on extreme precipitation by aerosols relative to greenhouse gases is still not well known. Here we use the simulations from the Precipitation Driver and Response Model Intercomparison Project that enable us to compare changes in mean and extreme precipitation due to greenhouse gases with those due to black carbon and sulfate aerosols, using indicators for dry extremes as well as for moderate and very extreme precipitation. Generally, we find that the more extreme a precipitation event is, the more pronounced is its response relative to global mean surface temperature change, both for aerosol and greenhouse gas changes. Black carbon (BC) stands out with distinct behavior and large differences between individual models. Dry days become more frequent with BC-induced warming compared to greenhouse gases, but so does the intensity and frequency of extreme precipitation. An increase in sulfate aerosols cools the surface and thereby the atmosphere, and thus induces a reduction in precipitation with a stronger effect on extreme than on mean precipitation. A better understanding and representation of these processes in models will provide knowledge for developing strategies for both climate change and air pollution mitigation.
Stjern CW, Lund MT, Samset BH, et al., 2019, Arctic amplification response to individual climate drivers, Journal of Geophysical Research: Atmospheres, Vol: 124, Pages: 6698-6717, ISSN: 2169-897X
The Arctic is experiencing rapid climate change in response to changes in greenhouse gases, aerosols, and other climate drivers. Emission changes in general, as well as geographical shifts in emissions and transport pathways of short‐lived climate forcers, make it necessary to understand the influence of each climate driver on the Arctic. In the Precipitation Driver Response Model Intercomparison Project, 10 global climate models perturbed five different climate drivers separately (CO2, CH4, the solar constant, black carbon, and SO4). We show that the annual mean Arctic amplification (defined as the ratio between Arctic and the global mean temperature change) at the surface is similar between climate drivers, ranging from 1.9 (± an intermodel standard deviation of 0.4) for the solar to 2.3 (±0.6) for the SO4 perturbations, with minimum amplification in the summer for all drivers. The vertical and seasonal temperature response patterns indicate that the Arctic is warmed through similar mechanisms for all climate drivers except black carbon. For all drivers, the precipitation change per degree global temperature change is positive in the Arctic, with a seasonality following that of the Arctic amplification. We find indications that SO4 perturbations produce a slightly stronger precipitation response than the other drivers, particularly compared to CO2.
Tang T, Shindell D, Faluvegi G, et al., 2019, Comparison of effective radiative forcing calculations using multiple methods, drivers, and models, Journal of Geophysical Research: Atmospheres, Vol: 124, Pages: 4382-4394, ISSN: 2169-897X
American Geophysical Union. All Rights Reserved. We compare six methods of estimating effective radiative forcing (ERF) using a set of atmosphere-ocean general circulation models. This is the first multiforcing agent, multimodel evaluation of ERF values calculated using different methods. We demonstrate that previously reported apparent consistency between the ERF values derived from fixed sea surface temperature simulations and linear regression holds for most climate forcings, excluding black carbon (BC). When land adjustment is accounted for, however, the fixed sea surface temperature ERF values are generally 10–30% larger than ERFs derived using linear regression across all forcing agents, with a much larger (~70–100%) discrepancy for BC. Except for BC, this difference can be largely reduced by either using radiative kernel techniques or by exponential regression. Responses of clouds and their effects on shortwave radiation show the strongest variability in all experiments, limiting the application of regression-based ERF in small forcing simulations.
Nowack P, Ong QYE, Braesicke P, et al., 2019, Machine learning parameterizations for ozone in climate sensitivity simulations, Kurzfassungen der Meteorologentagung DACH
Richardson TB, Forster PM, Andrews T, et al., 2018, Drivers of Precipitation Change: An Energetic Understanding, Journal of Climate, Vol: 31, Pages: 9641-9657, ISSN: 0894-8755
The response of the hydrological cycle to climate forcings can be understood within the atmospheric energy budget framework. In this study precipitation and energy budget responses to five forcing agents are analyzed using 10 climate models from the Precipitation Driver Response Model Intercomparison Project (PDRMIP). Precipitation changes are split into a forcing-dependent fast response and a temperature-driven hydrological sensitivity. Globally, when normalized by top-of-atmosphere (TOA) forcing, fast precipitation changes are most sensitive to strongly absorbing drivers (CO2, black carbon). However, over land fast precipitation changes are most sensitive to weakly absorbing drivers (sulfate, solar) and are linked to rapid circulation changes. Despite this, land-mean fast responses to CO2 and black carbon exhibit more intermodel spread. Globally, the hydrological sensitivity is consistent across forcings, mainly associated with increased longwave cooling, which is highly correlated with intermodel spread. The land-mean hydrological sensitivity is weaker, consistent with limited moisture availability. The PDRMIP results are used to construct a simple model for land-mean and sea-mean precipitation change based on sea surface temperature change and TOA forcing. The model matches well with CMIP5 ensemble mean historical and future projections, and is used to understand the contributions of different drivers. During the twentieth century, temperature-driven intensification of land-mean precipitation has been masked by fast precipitation responses to anthropogenic sulfate and volcanic forcing, consistent with the small observed trend. However, as projected sulfate forcing decreases, and warming continues, land-mean precipitation is expected to increase more rapidly, and may become clearly observable by the mid-twenty-first century.
Smith CJ, Kramer RJ, Myhre G, et al., 2018, Understanding rapid adjustments to diverse forcing agents, Geophysical Research Letters, Vol: 45, Pages: 12023-12031, ISSN: 0094-8276
Rapid adjustments are responses to forcing agents that cause a perturbation to the top of atmosphere energy budget but are uncoupled to changes in surface warming. Different mechanisms are responsible for these adjustments for a variety of climate drivers. These remain to be quantified in detail. It is shown that rapid adjustments reduce the effective radiative forcing (ERF) of black carbon by half of the instantaneous forcing, but for CO2 forcing, rapid adjustments increase ERF. Competing tropospheric adjustments for CO2 forcing are individually significant but sum to zero, such that the ERF equals the stratospherically adjusted radiative forcing, but this is not true for other forcing agents. Additional experiments of increase in the solar constant and increase in CH4 are used to show that a key factor of the rapid adjustment for an individual climate driver is changes in temperature in the upper troposphere and lower stratosphere.
Myhre G, Kramer RJ, Smith CJ, et al., 2018, Quantifying the importance of rapid adjustments for global precipitation changes, Geophysical Research Letters, Vol: 20, Pages: 11399-11405, ISSN: 0094-8276
Different climate drivers influence precipitation in different ways. Here we use radiative kernels to understand the influence of rapid adjustment processes on precipitation in climate models. Rapid adjustments are generally triggered by the initial heating or cooling of the atmosphere from an external climate driver. For precipitation changes, rapid adjustments due to changes in temperature, water vapor, and clouds are most important. In this study we have investigated five climate drivers (CO2, CH4, solar irradiance, black carbon, and sulfate aerosols). The fast precipitation responses to a doubling of CO2 and a 10-fold increase in black carbon are found to be similar, despite very different instantaneous changes in the radiative cooling, individual rapid adjustments, and sensible heating. The model diversity in rapid adjustments is smaller for the experiment involving an increase in the solar irradiance compared to the other climate driver perturbations, and this is also seen in the precipitation changes.
Shawki D, Voulgarakis A, Chakraborty A, et al., 2018, The South Asian monsoon response to remote aerosols: global and regional mechanisms, Journal of Geophysical Research, Vol: 123, Pages: 11585-11601, ISSN: 0148-0227
The South Asian summer monsoon has been suggested to be influenced by atmospheric aerosols, and this influence can be the result of either local or remote emissions. We have used the Hadley Centre Global Environment Model Version 3 (HadGEM3) coupled atmosphere‐ocean climate model to investigate for the first time the centennial‐scale South Asian precipitation response to emissions of sulfur dioxide (SO2), the dominant anthropogenic precursor of sulfate aerosol, from different midlatitude regions. Despite the localized nature of the regional heating that results from removing SO2 emissions, all experiments featured a similar large‐scale precipitation response over South Asia, driven by ocean‐modulated changes in the net cross‐equatorial heat transport and an opposing cross‐equatorial northward moisture transport. The effects are linearly additive, with the sum of the responses from the experiments where SO2 is removed from the United States, Europe, and East Asia resembling the response seen in the experiment where emissions are removed from the northern midlatitudes as a whole, but with East Asia being the largest contributor, even per unit of emission or top‐of‐atmosphere radiative forcing. This stems from the fact that East Asian emissions can more easily influence regional land‐sea thermal contrasts and sea level pressure differences that drive the monsoon circulation, compared to emissions from more remote regions. Our results suggest that radiative effects of remote pollution should not be neglected when examining changes in South Asian climate and that and it is important to examine such effects in coupled ocean‐atmosphere modeling frameworks.
Nowack PJ, Braesicke P, Haigh J, et al., 2018, Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations, Environmental Research Letters, Vol: 13, ISSN: 1748-9326
A number of studies have demonstrated the importance of ozone in climate change simulations, for example concerning global warming projections and atmospheric dynamics. However, fully interactive atmospheric chemistry schemes needed for calculating changes in ozone are computationally expensive. Climate modelers therefore often use climatological ozone fields, which are typically neither consistent with the actual climate state simulated by each model nor with the specific climate change scenario. This limitation applies in particular to standard modeling experiments such as preindustrial control or abrupt 4xCO2 climate sensitivity simulations. Here we suggest a novel method using a simple linear machine learning regression algorithm to predict ozone distributions for preindustrial and abrupt 4xCO2 simulations. Using the atmospheric temperature field as the only input, the regression reliably predicts three-dimensional ozone distributions at monthly to daily time intervals. In particular, the representation of stratospheric ozone variability is much improved compared with a fixed climatology, which is important for interactions with dynamical phenomena such as the polar vortices and the Quasi-Biennial Oscillation. Our method requires training data covering only a fraction of the usual length of simulations and thus promises to be an important stepping stone towards a range of new computationally efficient methods to consider ozone changes in long climate simulations. We highlight key development steps to further improve and extend the scope of machine learning-based ozone parameterizations.
Ryan E, Wild O, Voulgarakis A, et al., 2018, Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output, GEOSCIENTIFIC MODEL DEVELOPMENT, Vol: 11, Pages: 3131-3146, ISSN: 1991-959X
Global sensitivity analysis (GSA) is a powerful approach in identifying which inputs or parameters most affect a model's output. This determines which inputs to include when performing model calibration or uncertainty analysis. GSA allows quantification of the sensitivity index (SI) of a particular input – the percentage of the total variability in the output attributed to the changes in that input – by averaging over the other inputs rather than fixing them at specific values. Traditional methods of computing the SIs using the Sobol and extended Fourier Amplitude Sensitivity Test (eFAST) methods involve running a model thousands of times, but this may not be feasible for computationally expensive Earth system models. GSA methods that use a statistical emulator in place of the expensive model are popular, as they require far fewer model runs. We performed an eight-input GSA, using the Sobol and eFAST methods, on two computationally expensive atmospheric chemical transport models using emulators that were trained with 80 runs of the models. We considered two methods to further reduce the computational cost of GSA: (1) a dimension reduction approach and (2) an emulator-free approach. When the output of a model is multi-dimensional, it is common practice to build a separate emulator for each dimension of the output space. Here, we used principal component analysis (PCA) to reduce the output dimension, built an emulator for each of the transformed outputs, and then computed SIs of the reconstructed output using the Sobol method. We considered the global distribution of the annual column mean lifetime of atmospheric methane, which requires ∼ 2000 emulators without PCA but only 5–40 emulators with PCA. We also applied an emulator-free method using a generalised additive model (GAM) to estimate the SIs using only the training runs. Compared to the emulator-only methods, the emulator–PCA and GAM methods accurately estimated the SIs
Kasoar MR, Shawki D, Voulgarakis A, 2018, Similar spatial patterns of global climate response to aerosols from different regions, npj Climate and Atmospheric Science, Vol: 12, ISSN: 2397-3722
Anthropogenic aerosol forcing is spatially heterogeneous, mostly localised around industrialised regions like North America, Europe, East and South Asia. Emission reductions in each of these regions will force the climate in different locations, which could have diverse impacts on regional and global climate. Here, we show that removing sulphur dioxide (SO2) emissions from any of these northern-hemisphere regions in a global composition-climate model results in significant warming across the hemisphere, regardless of the emission region. Although the temperature response to these regionally localised forcings varies considerably in magnitude depending on the emission region, it shows a preferred spatial pattern independent of the location of the forcing. Using empirical orthogonal function analysis, we show that the structure of the response is tied to existing modes of internal climate variability in the model. This has implications for assessing impacts of emission reduction policies, and our understanding of how climate responds to heterogeneous forcings.
Tang T, Shindell D, Samset BH, et al., 2018, Dynamical response of Mediterranean precipitation to greenhouse gases and aerosols, ATMOSPHERIC CHEMISTRY AND PHYSICS, Vol: 18, Pages: 8439-8452, ISSN: 1680-7316
Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and, thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the beginning of the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on Mediterranean precipitation remain largely unknown. Here we compare the modeled dynamical response of Mediterranean precipitation to individual forcing agents in a set of global climate models (GCMs). Our analyses show that both greenhouse gases and aerosols can cause drying in the Mediterranean and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive sea level pressure (SLP) pattern similar to the North Atlantic Oscillation–Arctic Oscillation, characterized by higher SLP at midlatitudes and lower SLP at high latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 1901–2010, roughly one-third (31±17%) of the precipitation decrease would be attributable to global BC forcing with the remainder largely attributable to WMGHGs, whereas global scattering sulfate aerosols would have negligible impacts. Aerosol–cloud interactions appear to have minimal impacts on Mediterranean precipitation in these models, at least in part because many simulations did not fully include such processes; these merit further study. The findings from this study suggest that future BC and WMGHG emissions may significantly affect regional water resources, agricultural practices, ecosystems and
Liu L, Shawki D, Voulgarakis A, et al., 2018, A PDRMIP multi-model study on the impacts of regional aerosol forcings on global and regional precipitation, Journal of Climate, Vol: 31, Pages: 4429-4447, ISSN: 0894-8755
Atmospheric aerosols such as sulfate and black carbon (BC) generate inhomogeneous radiative forcing and can affect precipitation in distinct ways compared to greenhouse gases (GHGs). Their regional effects on the atmospheric energy budget and circulation can be important for understanding and predicting global and regional precipitation changes, which act on top of the background GHG-induced hydrological changes. Under the framework of the Precipitation Driver Response Model Inter-comparison Project (PDRMIP), multiple models were used for the first time to simulate the influence of regional (Asian and European) sulfate and BC forcing on global and regional precipitation. The results show that, as in the case of global aerosol forcing, the global fast precipitation response to regional aerosol forcing scales with global atmospheric absorption, and the slow precipitation response scales with global surface temperature response. Asian sulphate aerosols appear to be a stronger driver of global temperature and precipitation change compared to European aerosols, but when the responses are normalised by unit radiative forcing or by aerosol burden change, the picture reverses, with European aerosols being more efficient in driving global change. The global apparent hydrological sensitivities of these regional forcing experiments are again consistent with those for corresponding global aerosol forcings found in the literature. However, the regional responses and regional apparent hydrological sensitivities do not align with the corresponding global values. Through a holistic approach involving analysis of the energy budget combined with exploring changes in atmospheric dynamics, we provide a framework for explaining the global and regional precipitation responses to regional aerosol forcing.
Myhre G, Samset BH, Hodnebrog Ø, et al., 2018, Sensible heat has significantly affected the global hydrological cycle over the historical period, Nature Communications, Vol: 9, ISSN: 2041-1723
Globally, latent heating associated with a change in precipitation is balanced by changes to atmospheric radiative cooling and sensible heat fluxes. Both components can be altered by climate forcing mechanisms and through climate feedbacks, but the impacts of climate forcing and feedbacks on sensible heat fluxes have received much less attention. Here we show, using a range of climate modelling results, that changes in sensible heat are the dominant contributor to the present global-mean precipitation change since preindustrial time, because the radiative impact of forcings and feedbacks approximately compensate. The model results show a dissimilar influence on sensible heat and precipitation from various drivers of climate change. Due to its strong atmospheric absorption, black carbon is found to influence the sensible heat very differently compared to other aerosols and greenhouse gases. Our results indicate that this is likely caused by differences in the impact on the lower tropospheric stability.
Richardson TB, Forster PM, Andrews T, et al., 2018, Carbon Dioxide Physiological Forcing Dominates Projected Eastern Amazonian Drying, Geophysical Research Letters, Vol: 45, Pages: 2815-2825, ISSN: 0094-8276
Future projections of east Amazonian precipitation indicate drying, but they are uncertain and poorly understood. In this study we analyze the Amazonian precipitation response to individual atmospheric forcings using a number of global climate models. Black carbon is found to drive reduced precipitation over the Amazon due to temperature-driven circulation changes, but the magnitude is uncertain. CO 2 drives reductions in precipitation concentrated in the east, mainly due to a robustly negative, but highly variable in magnitude, fast response. We find that the physiological effect of CO 2 on plant stomata is the dominant driver of the fast response due to reduced latent heating and also contributes to the large model spread. Using a simple model, we show that CO 2 physiological effects dominate future multimodel mean precipitation projections over the Amazon. However, in individual models temperature-driven changes can be large, but due to little agreement, they largely cancel out in the model mean.
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