Variability In Climate Models Research Articles

UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models

Abstract Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias correction approaches usually focus on correcting lower-order statistics (mean, standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.

Partitioning climate uncertainty in ecological projections: Pacific oysters in a hotter Europe

Projections of the range expansions of marine species are critical if we are to anticipate and mitigate the impacts of climate change on marine ecosystems. However, most projections do not assess the level of uncertainty of future changes, which brings their usefulness for scenario planning and ecosystem management into question. For the overall climate system, these uncertainties take three forms: scenario uncertainty, climate model uncertainty and internal climate variability. Critically, internal variability, a measure of how natural variability affects future climate projections, has largely been ignored in ecological studies. Here we use an ensemble modelling approach for the non-native Pacific oyster in Europe to understand the impact of these uncertainties. Future Pacific oyster recruitment was projected using a model that relates recruitment to cumulative and instantaneous heat exposure. Model projections were carried out for four climate change scenarios: SSP1 2.6, SSP2 4.5, SSP3 7.0 and SSP5 8.5. In each scenario an ensemble of over twenty climate models was used. The impact of internal variability in climate models was assessed by using five climate models which were available with multiple pre-industrial starting points. We find that model uncertainty within SSP1 2.6 is higher than the differences between SSP1 2.6 and SSP 4.5, but it is unclear if overall scenario uncertainty is greater than climate model uncertainty due to its subjective nature. Comparisons of scenario projections indicate that future recruitment areas of Pacific oysters under the SSP5 8.5 scenario could be more than twice as high as in the low emissions SSP1 2.6 scenario. Importantly, the ensemble showed that near-term changes in Pacific oysters are highly uncertain due to internal variability, which is of a similar magnitude to climate model uncertainty on a 20-year timescale. Our results show that it is critical to think about the future in terms of potential scenarios and not individual projections.

Machine Learning for Nonorographic Gravity Waves in a Climate Model

Abstract There is growing use of machine learning algorithms to replicate subgrid parameterization schemes in global climate models. Parameterizations rely on approximations; thus, there is potential for machine learning to aid improvements. In this study, a neural network is used to mimic the behavior of the nonorographic gravity wave scheme used in the Met Office climate model, important for stratospheric climate and variability. The neural network is found to require only two of the six inputs used by the parameterization scheme, suggesting the potential for greater efficiency in this scheme. Use of a one-dimensional mechanistic model is advocated, allowing neural network hyperparameters to be chosen based on emergent features of the coupled system with minimal computational cost, and providing a testbed prior to coupling to a climate model. A climate model simulation, using the neural network in place of the existing parameterization scheme, is found to accurately generate a quasi-biennial oscillation of the tropical stratospheric winds, and correctly simulate the nonorographic gravity wave variability associated with El Niño–Southern Oscillation and stratospheric polar vortex variability. These internal sources of variability are essential for providing seasonal forecast skill, and the gravity wave forcing associated with them is reproduced without explicit training for these patterns. Significance Statement Climate simulations are required for providing advice to government, industry, and society regarding the expected climate on time scales of months to decades. Machine learning has the potential to improve the representation of some sources of variability in climate models that are too small to be directly simulated by the model. This study demonstrates that a neural network can simulate the variability due to atmospheric gravity waves that is associated with El Niño–Southern Oscillation and with the tropical and polar regions of the stratosphere. These details are important for a model to produce more accurate predictions of regional climate.

Bioaerosols are the dominant source of warm-temperature immersion-mode INPs and drive uncertainties in INP predictability.

Ice-nucleating particles (INPs) are rare atmospheric aerosols that initiate primary ice formation, but accurately simulating their concentrations and variability in large-scale climate models remains a challenge. Doing so requires both simulating major particle sources and parameterizing their ice nucleation (IN) efficiency. Validating and improving model predictions of INP concentrations requires measuring their concentrations delineated by particle type. We present a method to speciate INP concentrations into contributions from dust, sea spray aerosol (SSA), and bioaerosol. Field campaign data from Bodega Bay, California, showed that bioaerosols were the primary source of INPs between -12° and -20°C, while dust was a minor source and SSA had little impact. We found that recent parameterizations for dust and SSA accurately predicted ambient INP concentrations. However, the model did not skillfully simulate bioaerosol INPs, suggesting a need for further research to identify major factors controlling their emissions and INP efficiency for improved representation in models.

Large-scale sea ice-Surface temperature variability linked to Atlantic meridional overturning circulation.

Due to its involvement in numerous feedbacks, sea ice plays a crucial role not only for polar climate but also at global scale. We analyse state-of-the-art observed, reconstructed, and modelled sea-ice concentration (SIC) together with sea surface temperature (SST) to disentangle the influence of different forcing factors on the variability of these coupled fields. Canonical Correlation Analysis provides distinct pairs of coupled Arctic SIC-Atlantic SST variability which are linked to prominent oceanic and atmospheric modes of variability over the period 1854-2017. The first pair captures the behaviour of the Atlantic meridional overturning circulation (AMOC) while the third and can be associated with the North Atlantic Oscillation (NAO) in a physically consistent manner. The dominant global SIC-Atlantic SST coupled mode highlights the contrast between the responses of Arctic and Antarctic sea ice to changes in AMOC over the 1959-2021 period. Model results indicate that coupled SST-SIC patterns can be associated with changes in ocean circulation. We conclude that a correct representation of AMOC-induced coupled SST-SIC variability in climate models is essential to understand the past, present and future sea-ice evolution.

Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)

Abstract. The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected, task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian Basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for the pre-industrial climate, while for the future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated for by much drier soils (−45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties in the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties in landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.

Internal climate variability and spatial temperature correlations during the past 2000 years

Abstract. The spatio-temporal structure of natural climate variability has to be taken into account when unravelling observed climatic changes and simulating future climate change. However, based on the comparison of temperature reconstructions and climate model simulations covering the past 2 millennia, it has been argued that climate models are biased. They would simulate too little temporal temperature variability and too high correlations between temperature time series from different continents. One of the proposed causes is the lack of internal climate variability in climate models on centennial timescales, for instance variability related to the Atlantic meridional overturning circulation (AMOC). We present a perturbed-parameter ensemble with the iLOVECLIM Earth system model containing various levels of AMOC-related internal climate variability to investigate the effect on the spatio-temporal temperature variability structure. The model ensemble shows that enhanced AMOC variability indeed leads to more continental-scale temperature variability, but it also increases the spatio-temporal temperature correlations between different continents. However, combining the iLOVECLIM results with CMIP5 model results and various PAGES-2k temperature field reconstructions, we show overall agreement for the magnitude of continental temperature variability in models and reconstructions, but both the simulated and the reconstructed ranges are large. This is even more true when considering higher-order metrics like inter-continental temperature correlations or temperature variability land–sea contrasts. For such metrics, uncertainties in both model results and temperature reconstructions are so large that they hamper our ability to constrain simulated spatio-temporal structure of centennial temperature variability. As a result, we cannot determine the importance of AMOC variability for the climatic evolution over the past 2 millennia.

Modeled Interannual Variability of Arctic Sea Ice Cover is within Observational Uncertainty

Abstract Internal variability is the dominant cause of projection uncertainty of Arctic sea ice in the short and medium term. However, it is difficult to determine the realism of simulated internal variability in climate models, as observations only provide one possible realization while climate models can provide numerous different realizations. To enable a robust assessment of simulated internal variability of Arctic sea ice, we use a resampling technique to build synthetic ensembles for both observations and climate models, focusing on interannual variability, which is the dominant time scale of Arctic sea ice internal variability. We assess the realism of the interannual variability of Arctic sea ice cover as simulated by six models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) that provide large ensembles compared to four observational datasets. We augment the standard definition of model and observational consistency by representing the full distribution of resamplings, analogous to the distribution of variability that could have randomly occurred. We find that modeled interannual variability typically lies within observational uncertainty. The three models with the smallest mean state biases are the only ones consistent in the pan-Arctic for all months, but no model is consistent for all regions and seasons. Hence, choosing the right model for a given task as well as using internal variability as an additional metric to assess sea ice simulations is important. The fact that CMIP5 large ensembles broadly simulate interannual variability consistent within observational uncertainty gives confidence in the internal projection uncertainty for Arctic sea ice based on these models. Significance Statement The purpose of this study is to evaluate the historical simulated internal variability of Arctic sea ice in climate models. Determining model realism is important to have confidence in the projected sea ice evolution from these models, but so far only mean state and trends are commonly assessed metrics. Here we assess internal variability with a focus on the interannual variability, which is the dominant time scale for internal variability. We find that, in general, models agree well with observations, but as no model is within observational uncertainty for all months and locations, choosing the right model for a given task is crucial. Further refinement of internal variability realism assessments will require reduced observational uncertainty.

Megadroughts in the Common Era and the Anthropocene

Exceptional drought events, known as megadroughts, have occurred on every continent outside Antarctica over the past ~2,000 years, causing major ecological and societal disturbances. In this Review, we discuss shared causes and features of Common Era (Year 1–present) and future megadroughts. Decadal variations in sea surface temperatures are the primary driver of megadroughts, with secondary contributions from radiative forcing and land–atmosphere interactions. Anthropogenic climate change has intensified ongoing megadroughts in south-western North America and across Chile and Argentina. Future megadroughts will be substantially warmer than past events, with this warming driving projected increases in megadrought risk and severity across many regions, including western North America, Central America, Europe and the Mediterranean, extratropical South America, and Australia. However, several knowledge gaps currently undermine confidence in understanding past and future megadroughts. These gaps include a paucity of high-resolution palaeoclimate information over Africa, tropical South America and other regions; incomplete representations of internal variability and land surface processes in climate models; and the undetermined capacity of water-resource management systems to mitigate megadrought impacts. Addressing these deficiencies will be crucial for increasing confidence in projections of future megadrought risk and for resiliency planning.

More Intense, Organized Deep Convection With Shrinking Tropical Ascent Regions

AbstractChanges in the frequency and morphology of convection in a warming climate have major implications for the hydrologic cycle and continue to be a large source of variability in climate models. Recent studies of model output indicate a narrowing and intensification of the ascending branch of the Hadley circulation (i.e., the Intertropical Convergence Zone). The present work uses tropical ascent area fraction (Au) and Tropical Rainfall Measurement Mission precipitation features to study global tropical variability in convection with ascent area changes in the current climate. Ascent region convection becomes more intense and organized when Au is low. Overall increases in mean precipitation as the tropical ascent regions shrink are primarily driven by an increase in the areal extent of convective systems, rather than intensity of the convective system rainfall, suggesting an increase in the organization and aggregation of convection as the climate continues to warm.

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks

AbstractThe global mean surface temperature (GMST) record exhibits both interannual to multidecadal variability and a long‐term warming trend due to external climate forcing. To explore the predictability of temporary slowdowns in decadal warming, we apply an artificial neural network (ANN) to climate model data from the Community Earth System Model Version 2 Large Ensemble. Here, an ANN is tasked with whether or not there will be a slowdown in the rate of the GMST trend by using maps of ocean heat content (OHC) at the onset. Through a machine learning explainability method, we find the ANN is learning off‐equatorial patterns of anomalous OHC that resemble transitions in the phase of the Interdecadal Pacific Oscillation in order to make slowdown predictions. Finally, we test our ANN on observed historical data, which further reveals how explainable neural networks are useful tools for understanding decadal variability in both climate models and observations.

Impact of climate change on streamflow of Melka Wakena catchment, Upper Wabi Shebelle sub-basin, south-eastern Ethiopia

Abstract Climate change is becoming a global concern, dictating the water resource availability of a certain region. This study focuses on the impact of climate change on the streamflow of the Melka Wakena reservoir in the Upper Wabi Shebelle sub-basin using a Soil Water Analysis Tool (SWAT). The climate model variables (precipitation and temperature) are obtained from the Coordinated Regional Downscaling Experiment (CORDEX-Africa) and applied under representative concentration pathway (RCP4.5) and (RCP8.5) scenarios. Bias correction was applied to the climate variables before transferring to the hydrological model (SWAT) to simulate discharge. The performance measures R2, NSE, and Percent Bias (PBIAS) for calibration and validation were 0.69, 0.65, and −5.10 and 0.65, 0.61, and −9.81, respectively. Streamflow was simulated for two consecutive periods from 2021 to 2050 and from 2051 to 2080 for both scenarios and compared with the base period from 1986 to 2015 to explore the impact of climate change on inflows to the reservoir. The result obtained showed that the precipitation was predicted to be mildly decreasing, whereas overall annual flow was projected to be insignificantly decreasing under RCP4.5 and RCP8.5 scenarios. The impact of climate change on the water resource of the study area was predicted to be a statistically insignificant reduction.

Understanding Differences in North Atlantic Poleward Ocean Heat Transport and Its Variability in Global Climate Models

AbstractThe ocean heat transport from the North Atlantic to the Barents Sea impacts the sea ice extent and the energy budget of the Arctic. The analyzed climate models from the fifth (CMIP5) and sixth (CMIP6) phases of the Coupled Model Intercomparison Project show large intermodel differences in the ocean heat transport with biases of several Terawatts at the Iceland‐Scotland Ridge and Barents Sea Opening (BSO). While both model generations show a large spread in mean volume transports, in CMIP6 temperatures are more homogeneous and realistic, yielding heat transports closer to observations. On all time scales, changes in heat transport reflect changes in volume transport, while temperature changes affect the heat transport variability on longer time scales, especially at the BSO. The temporal variability of heat and volume transports is linked to wind forcing south of Iceland and along the Norwegian coast in all models but has different magnitudes.

Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1): Multivariate bias‐corrected climate model outputs for terrestrial modelling and attribution studies in North America

AbstractThe Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1) contains 50‐member ensembles of bias‐adjusted near‐surface global and regional climate model variables on a 0.5° grid over North America for historical and future scenarios (1950–2100). Canadian Earth System Model Large Ensembles (CanESM2 LE) and Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) datasets are bias‐corrected using a multivariate quantile‐mapping algorithm for statistical consistency – in terms of marginal distributions and multivariate dependence structure – with two observationally constrained historical meteorological forcing datasets. For each observational dataset, bias‐adjusted variables are provided for two sets of 50‐member initial‐condition CanESM2 ensembles (historical plus RCP8.5 scenarios, 1950–2005 and 2006–2100, respectively; and historicalNAT scenario, 1950–2020, which excludes anthropogenic forcings), and one 50‐member CanRCM4 ensemble (historical plus RCP8.5). The archive includes daily minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation and incoming longwave radiation. Intended uses include hydrological and land surface impact modelling, as well as related event attribution studies.

Surface temperature variability in climate models with large and small internal climate variability

AbstractBy analyzing large ensemble simulations using the Community Earth System Model (CESM_LE), the Max Planck Institute Earth System Model Grand Ensemble (MPI_GE), and Coupled Model Intercomparison Project phase 5 (CMIP5) climate models, we quantified internal climate variability (ICV) of surface temperature in each model based on the spread of simulated global mean surface temperature from the ensemble mean. Then, we examined the characteristics of simulated surface temperature variability in climate models with large and small ICV in the present climate and in a future climate. Both the CESM_LE and MPI_GE members with large ICVs tended to simulate larger surface temperature variability at low latitudes, including El Niño and Southern Oscillation (ENSO) variability, and larger cooling and warming trends of the global mean surface temperatures than those with small ICVs in the present climate. Similar characteristics were observed in CMIP5 climate models with large and small ICVs in the present climate. This implies that surface temperature variability including extreme climate events should be cautiously examined in climate models with large and small ICVs. On the other hand, the characteristics of surface temperature variability simulated in the CMIP5 climate models with large or small ICVs were similar from the present climate to future climate with magnitude of ICVs. This was in contrast to that simulated in the CESM_LE and MPI_GE, in which the magnitude of ICV changes between the present climate and the future climate. We inferred that these differences between CMIP5 climate models and large ensemble simulations could primarily be attributed to intermodel differences in the CMIP5 climate models, including model physics and parameterizations.

Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package

AbstractEl Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.

Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

AbstractStudies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

Differential responses of native and managed prairie pastures to environmental variability and management practices

Future weather and climates, especially rainfall, are expected to have larger variability in the Southern Plains of the United States. However, the degree and timing of environmental variability that affect productivity of pastures managed differently have not been well studied. We examined the impacts of environmental variability on grassland productivity using 17 years of gross primary productivity (GPP) data for co-located native and managed prairie pastures in Oklahoma. We also considered the interactive effects of management factors and environmental variability into the regression models and identified the critical temporal windows of environmental variables (CWE) that influence annual variability in GPP. Managed pasture (MP) showed greater variability of GPP than did native pasture (NP), particularly with reduced GPP in drought years. The resilience of native prairies under unfavorable climate extremes was evident by lower GPP anomalies in NP than MP during the 2011–2012 drought. Although both pastures experienced the same degree of environmental variability, the CWE affecting GPP was significantly different between NP and MP due to the modulating impact of management practices on the responses of GPP. Not only the range but also the timings of the CWE were different between NP and MP as MP was more responsive to the spring temperature and fall rainfall. Our findings warrant the incorporation of MP as a different commodity from NP when accounting for the ecosystem responses to environmental variability in global climate models.

Importance of a vertically tilting structure for energizing the North Atlantic Oscillation

The North Atlantic Oscillation (NAO) is a prominent mode of atmospheric variability that influences weather and climate, including the occurrence of extreme events, over a large part of Europe and Northeastern America. The NAO has been considered to be maintained primarily by migratory weather disturbances and to have a deep structure with no vertical tilt. A careful inspection nonetheless reveals that the associated anomalies do exhibit a subtle vertical tilt, but its dynamical implications are still unknown. Here we show that this vertical tilt is of vital dynamical significance for the wintertime NAO. We find, using atmospheric reanalysis data, that the tilted anomalies transport heat across the pronounced thermal gradient associated with a background westerly jetstream, advecting air from the cooler North America and Greenland to the warmer Atlantic, thereby acting to reinforce NAO’s thermal anomalies. The resultant conversion of potential energy from the background state is a larger energy source for maintaining the NAO than the feedback from migratory disturbances. Our findings thus uncover a fundamental mechanism of the NAO dynamics, with implications for the improvement of seasonal predictions for the Euro-Atlantic climate and the representation of the NAO variability in climate models.

Intraseasonal Vertical Cloud Regimes Based on CloudSat Observations over the Tropics

This study identifies the evolution of tropical vertical cloud regimes (CRs) and their associated heating structures on the intraseasonal time scales. Using the cloud classification retrievals of CloudSat during boreal winter between 2006 and 2017, the CR index is defined as the leading pair of the combined multivariate empirical orthogonal functions of the daily mean frequency of deep, high, and low clouds over the tropical Indian Ocean, Maritime Continents, and the Western Pacific. The principal components of the CR index exhibit robust temporal variance in the 30 to 80 day intraseasonal band. Based on the propagation stages of the CRs, the coherent vertical structures of cloud composition and large-scale moisture and vertical motion exhibit a westward-tilted structure. The associated Q1-QR diabatic heating and cloud radiative forcing are consistent with the key characteristics of the Madden Julian Oscillation (MJO) documented in the previous studies. Lastly, an MJO case study showcases that the presented approach characteristically captures the propagation of moisture, cloud vertical structure, and precipitation activity across spatial and temporal scales. The current results suggest that the CR index can potentially serve as an evaluation metric to cloud-associated processes in the simulated tropical intraseasonal variability in global climate models.