Uncertainty In Climate Change Projections Research Articles

Resolution dependence of southern Atlantic Ocean stratocumulus decks

AbstractOceanic stratocumulus decks of clouds are among the largest contributors to the Earth's radiation budget, covering around a fifth of the planet's surface and reflecting a large part of the incoming solar radiation. Unfortunately, these clouds are not well represented in modern climate models, resulting in one of the leading causes of uncertainty in climate change projections. This contribution analyses this issue from a novel perspective and sheds light on the mechanisms behind the misrepresentation and resolution dependence of these large clouds. The analysis is based on realistic week‐long simulations performed over a km oceanic domain. Four horizontal resolutions, between 4.4 and 0.55 km, are employed, resulting in a timely investigation, especially in light of the high resolutions employed by present and near‐future climate projections. Results show that the liquid cloud water, the main contributor to the simulated grid‐scale clouds, decreases with a power‐law decay as the resolution increases, whereas the water vapour, responsible for subgrid‐scale clouds, is much less affected by the grid spacing. The leading cause is identified as an imbalance between the rates of change of the advection and turbulence parametrisation terms. In order to verify this observation and provide a possible mitigation to the issue, a second set of simulations is performed where the turbulence parametrisation is tuned. The strategy proves to be successful, confirming the hypotheses and resulting not only in a resolution‐independent radiation budget but also cloud coverage.

Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India

AbstractGlobal climate model (GCM) projections are subject to significant uncertainties. Quantifying uncertainties in climate change projections improves credibility and makes climate data more reliable. This study aims to quantify the uncertainties in projected extreme precipitation during the 21st century over the homogeneous rainfall regions of India simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs. The percentile‐based square root error variance (SREV) method estimates model, scenario and ensemble uncertainties in projections of extreme precipitation. The uncertainty is investigated at four thresholds: 95th, 99th, 99.9th and 100th percentiles. The results show that the wet northeast region has a greater SREV, which is consistent with previous studies. At 99th and 99.9th percentiles, relative model SREV is dominant over the northeast (NE) region. However, at the 95th percentile high relative model SREV is found over the northwest (NW) region during southwest (June, July, August and September) and NE (October, November and December) monsoon seasons. Model uncertainty is the main source of uncertainty, followed by scenario and ensemble uncertainties. The study indicates that the arid NW region in India has a higher level of uncertainty than other regions with homogeneous rainfall. These findings will assist policymakers in planning infrastructure development in arid regions of India.

Leveraging Deep Learning for Climate Change Prediction Models : A Dive into Cutting-Edge Techniques

In recent years, deep learning has emerged as a promising approach for statistical downscaling, which involves generating high-resolution climate data from coarse low-resolution variables. However, concerns persist regarding the ability of these models to generalize to future climate change scenarios, primarily due to the assumption of stationarity. In this study, we propose the use of deep ensembles as a straightforward method to enhance the uncertainty quantification of statistical downscaling models. By improving the representation of uncertainty, these models offer superior planning capabilities against extreme weather events, which can have significant negative social and economic impacts. Given the absence of observational future data, we rely on pseudo-reality experiments to evaluate the effectiveness of deep ensembles in quantifying the uncertainty of climate change projections. The adoption of deep ensembles facilitates more robust risk assessment, addressing critical needs in various sectors for adapting to climate change challenges.

Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections.

Documenting the uncertainty of climate change projections is a fundamental objective of the inter-comparison exercises organized to feed into the Intergovernmental Panel on Climate Change (IPCC) reports. Usually, each modeling center contributes to these exercises with one or two configurations of its climate model, corresponding to a particular choice of "free parameter" values, resulting from a long and often tedious "model tuning" phase. How much uncertainty is omitted by this selection and how might readers of IPCC reports and users of climate projections be misled by its omission? We show here how recent machine learning approaches can transform the way climate model tuning is approached, opening the way to a simultaneous acceleration of model improvement and parametric uncertainty quantification. We show how an automatic selection of model configurations defined by different values of free parameters can produce different "warming worlds," all consistent with present-day observations of the climate system.

Leaf-level coordination principles propagate to the ecosystem scale

Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories – the leaf economics spectrum, the global spectrum of plant form and function, and the least-cost hypothesis – are also observed between community mean traits and ecosystem processes. We combined ecosystem functional properties from FLUXNET sites, vegetation properties, and community mean plant traits into three corresponding principal component analyses. We find that the leaf economics spectrum (90 sites), the global spectrum of plant form and function (89 sites), and the least-cost hypothesis (82 sites) all propagate at the ecosystem level. However, we also find evidence of additional scale-emergent properties. Evaluating the coordination of ecosystem functional properties may aid the development of more realistic global dynamic vegetation models with critical empirical data, reducing the uncertainty of climate change projections.

Characterizing and quantifying uncertainty in projections of climate change impacts on air quality

Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections—greenhouse gas (GHG) emissions scenario, model response, and internal variability—have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.

Assessment of impacts of climate change on rice and wheat in the Indo-Gangetic plains

In this paper, the climate change scenarios of A2 and B2 for 2070-2100 time scale (denoted as 2080) for several key locations of India and its impact on rice and wheat crops based on regional climate model (PRECIS) were described. The PRECIS projects an increase in temperature over most parts of India especially in the IGP (Indo-Gangetic Plains), the region that presently experiences relatively low temperatures. Extreme high temperature episodes and rainfall intensity days are projected to become more frequent and the monsoon rainfall is also projected to increase. Rabi (mid Nov-March) season is likely to experience higher increase in temperature which could impact and hence become threat to the crops which really require low temperature for their growth. Climatic variability is also projected to increase in both A2 and B2 scenarios. All these projected changes are likely to reduce the wheat and rice yields in Indo-Gangetic plains of India. It is likely that there will be more number of years with low yields occurs towards the end of the century. Such yield reductions in rice and wheat crops due to climate change are mediated through reduction in crop duration, grain number and grain filling duration. The yield loss will be more in A2 scenario compared to B2. These quantitative estimates still have uncertainties associated with them, largely due to uncertainties in climate change projections, future technology growth, availability of inputs such as water for irrigation, changes in crop management and genotype. These projections nevertheless provide a direction of likely change in crop productivity in future climate change scenarios.

Replicability of Annual and Seasonal Precipitation by CMIP5 and CMIP6 GCMs over East Asia

global climate models (GCMs) structure is a source of uncertainty in climate change projections. Therefore, evaluation of the performances of GCMs is crucial for development of adaptation and mitigation measures and risk reduction against climate change impacts. This study quantified the performances of 10 GCMs of the Coupled Model Intercomparison Project 5 (CMIP5) and their CMIP6 equivalents in replicating precipitation over East Asia during 1975–2005. The CMIP5 and CMIP6 GCMs and their multi-model ensembles (MMEs) were annually and seasonally compared with the Global Precipitation Climatology Centre (GPCC) precipitation. Compromise Programming was used to rank GCMs by aggregating their scores obtained using statistical performance indices. Taylor diagram was used to assess their relative performances and GCMs' ability to replicate seasonal precipitation distribution was evaluated. Results revealed most CMIP6 GCMs' have better replicability of GPCC precipitation than CMIP5 GCMs. The MME mean of the CMIP6 showed better replicability of annual and seasonal GPCC precipitation than that of CMIP5. The CMIP6 MME also showed efficiency in simulating the seasonal precipitation distribution over East Asia. This study indicates improvements of some CMIP5 GCMs in their CMIP6 equivalents over East Asia. Findings from this study can be significant to policy makers and engineers in reducing the uncertainties in prediction of events such as floods and droughts.

A storyline view of the projected role of remote drivers on summer air stagnation in Europe and the United States

Storylines of atmospheric circulation change, or physically self-consistent narratives of plausible future events, have recently been proposed as a non-probabilistic means to represent uncertainties in climate change projections. Here, we apply the storyline approach to 21st century projections of summer air stagnation over Europe and the United States. We use a Climate Model Intercomparison Project Phase 6 (CMIP6) ensemble to generate stagnation storylines based on the forced response of three remote drivers of the Northern Hemisphere mid-latitude atmospheric circulation: North Atlantic warming, North Pacific warming, and tropical versus Arctic warming. Under a high radiative forcing scenario (SSP5-8.5), models consistently project increases in stagnation over Europe and the U.S., but the magnitude and spatial distribution of changes vary substantially across CMIP6 ensemble members, suggesting that future projections are not well-constrained when using the ensemble mean alone. We find that the diversity of projected stagnation changes depends on the forced response of remote drivers in individual models. This is especially true in Europe, where differences of ∼2 summer stagnant days per degree of global warming are found amongst the different storyline combinations. For example, the greatest projected increase in stagnation for most European regions leads to the smallest increase in stagnation for southwestern Europe; i.e. limited North Atlantic warming combined with near-equitable tropical and Arctic warming. In the U.S., only the atmosphere over the northern Rocky Mountain states demonstrates comparable stagnation projection uncertainty, due to opposite influences of remote drivers on the meteorological conditions that lead to stagnation.

Understanding the future of big sagebrush regeneration: challenges of projecting complex ecological processes

AbstractRegeneration is an essential demographic step that affects plant population persistence, recovery after disturbances, and potential migration to track suitable climate conditions. Challenges of restoring big sagebrush (Artemisia tridentata) after disturbances including fire‐invasive annual grass interactions exemplify the need to understand the complex regeneration processes of this long‐lived, woody species that is widespread across the semiarid western U.S. Projected 21st century climate change is expected to increase drought risks and intensify restoration challenges. A detailed understanding of regeneration will be crucial for developing management frameworks for the big sagebrush region in the 21st century. Here, we used two complementary models to explore spatial and temporal relationships in the potential of big sagebrush regeneration representing (1) range‐wide big sagebrush regeneration responses in natural vegetation (process‐based model) and (2) big sagebrush restoration seeding outcomes following fire in the Great Basin and the Snake River Plains (regression‐based model). The process‐based model suggested substantial geographic variation in long‐term regeneration trajectories with central and northern areas of the big sagebrush region remaining climatically suitable, whereas marginal and southern areas are becoming less suitable. The regression‐based model suggested, however, that restoration seeding may become increasingly more difficult, illustrating the particularly difficult challenge of promoting sagebrush establishment after wildfire in invaded landscapes. These results suggest that sustaining big sagebrush on the landscape throughout the 21st century may climatically be feasible for many areas and that uncertainty about the long‐term sustainability of big sagebrush may be driven more by dynamics of biological invasions and wildfire than by uncertainty in climate change projections. Divergent projections of the two models under 21st century climate conditions encourage further study to evaluate potential benefits of re‐creating conditions of uninvaded, unburned natural big sagebrush vegetation for post‐fire restoration seeding, such as seeding in multiple years and, for at least much of the northern Great Basin and Snake River Plains, the control of the fire‐invasive annual grass cycle.

Nutrients cause consolidation of soil carbon flux to small proportion of bacterial community

Nutrient amendment diminished bacterial functional diversity, consolidating carbon flow through fewer bacterial taxa. Here, we show strong differences in the bacterial taxa responsible for respiration from four ecosystems, indicating the potential for taxon-specific control over soil carbon cycling. Trends in functional diversity, defined as the richness of bacteria contributing to carbon flux and their equitability of carbon use, paralleled trends in taxonomic diversity although functional diversity was lower overall. Among genera common to all ecosystems, Bradyrhizobium, the Acidobacteria genus RB41, and Streptomyces together composed 45–57% of carbon flow through bacterial productivity and respiration. Bacteria that utilized the most carbon amendment (glucose) were also those that utilized the most native soil carbon, suggesting that the behavior of key soil taxa may influence carbon balance. Mapping carbon flow through different microbial taxa as demonstrated here is crucial in developing taxon-sensitive soil carbon models that may reduce the uncertainty in climate change projections.

Aerobic growth index (AGI): An index to understand the impacts of ocean warming and deoxygenation on global marine fisheries resources

Ocean warming and deoxygenation are affecting the physiological performance of marine species by increasing their oxygen demand while reducing oxygen supply. Impacts on organisms (e.g., growth and reproduction) can eventually affect entire populations, altering macroecological dynamics and shifting species’ distribution ranges. To quantify the effect of warming and deoxygenation on marine organisms, Penn et al. (2018) and Deutsch et al. (2020) developed two metabolic indices that integrate physiological, biogeographic and climatic data. Here, we develop an alternative index, referred to as Aerobic Growth Index (AGI) based on an approach that integrates the von Bertalanffy growth and metabolic theory. We compare the results derived from the application of AGI with those of the two previously published metabolic indices for six species: Atlantic blue crab (Callinectes sapidus), sharpsnout seabream (Diplodus puntazzo), Atlantic cod (Gadus morhua), Australian spiny lobster (Panulirus cygnus), red drum (Sciaenops ocellatus) and common cuttlefish (Sepia officinalis). The baseline (1971–2000) habitat suitability values of AGI are significantly and positively correlated with both metabolic indices (R2 ≥ 0.92). All three indices also show similar spatial patterns and magnitudes of viable habitat loss by the end of the 21st century (2071–2100) relative to baseline conditions under a high greenhouse gas trajectory (Representative Concentration Pathway 8.5). Our results support the applicability and use of AGI to better understand the impacts of warming and deoxygenation on global marine fishery resources. Given the uncertainties surrounding mechanisms linking temperature, oxygen and biogeography, there is a need for different indicators to account for these uncertainties in climate change projections.

A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming

Carbon cycle feedbacks represent large uncertainties in climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants and especially on reductions in the turnover time of soil carbon (τs) with warming. An approximation to the latter term for the top one metre of soil (ΔCs,τ) can be diagnosed from projections made with the CMIP6 and CMIP5 Earth System Models (ESMs), and is found to span a large range even at 2 °C of global warming (−196 ± 117 PgC). Here, we present a constraint on ΔCs,τ, which makes use of current heterotrophic respiration and the spatial variability of τs inferred from observations. This spatial emergent constraint allows us to halve the uncertainty in ΔCs,τ at 2 °C to −232 ± 52 PgC.

Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs

This study quantified the uncertainties in historical and future average monthly precipitation based on different bias correction methods, General Circulation Models (GCMs), Representative Concentration Pathways (RCPs), projection periods, and locations within the study area (i.e., the coastal and inland areas of South Korea). The GCMs were downscaled using deep learning, random forest, and nine quantile mapping bias correction methods for 22 gauge stations in South Korea. Data from the Korean Meteorology Administration (1970–2005) were used as the reference data in this study. Two statistical measures, the standard deviation and interquartile range, were used to quantify the uncertainties. The probability distribution density was used to assess the similarity/variation in rainfall distributions. For the historical period, the uncertainty in the selection of bias correction methods was greater than that in the selection of GCMs, whereas the opposite pattern was observed for the projection period. The projection period had the lowest level of uncertainty in the selection of RCP scenarios, and for the future, the uncertainly related to the time period was slightly lower than that for the other sources but was much greater than that for the RCP selection. In addition, it was clear that the level of uncertainty of inland areas is much lower than that of coastal areas. The uncertainty in the selection of the GCMs was slightly greater than that in the selection of the bias correction method. Therefore, the uncertainty in the selection of coastal areas was intermediate between the selection of bias correction methods and GCMs. This paper contributes to an improved understanding of the uncertainties in climate change projections arising from various sources.

Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia

AbstractReduction of uncertainty in climate change projections is a major challenge in impact assessment and adaptation planning. General circulation models (GCMs) along with projection scenarios are the major sources of uncertainty in climate change projections. Therefore, the selection of appropriate GCMs for a region can significantly reduce uncertainty in climate projections. In this study, 20 GCMs were statistically evaluated in replicating the spatial pattern of monsoon propagation towards Peninsular Malaysia at annual and seasonal time frames against the 20th Century Reanalysis dataset. The performance evaluation metrics of the GCMs for different time frames were compromised using a state-of-art multi-criteria decision-making approach, compromise programming, for the selection of GCMs. Finally, the selected GCMs were interpolated to 0.25° × 0.25° spatial resolution and bias-corrected using the Asian Precipitation – Highly-Resolved Observational Integration Towards Evaluation (APHRODITE) rainfall as reference data. The results revealed the better performance of BCC-CSM1-1 and HadGEM2-ES in replicating the historical rainfall in Peninsular Malaysia. The bias-corrected projections of selected GCMs revealed a large variation of the mean, standard deviation and 95% percentile of daily rainfall in the study area for two futures, 2020–2059 and 2060–2099 compared to base climate.

The Assessment of Impacts and Risks of Climate Change on Agriculture (AIRCCA) model: a tool for the rapid global risk assessment for crop yields at a spatially explicit scale

ABSTRACT A main channel through which climate change is expected to affect the economy is the agricultural sector. Large spatial variability in these impacts and high levels of uncertainty in climate change projections create methodological challenges for assessing the consequences this sector could face. Crop emulators based on econometric fixed-effects models that can closely reproduce biophysical models are estimated. With these reduced form crop emulators, we develop AIRCCA, a user-friendly software for the assessment of impacts and risks of climate change on agriculture, that allows stakeholders to make a rapid global assessment of the effects of climate change on maize, wheat and rice yields. AIRCCA produces spatially explicit probabilistic impact scenarios and user-defined risk metrics for the main four Intergovernmental Panel on Climate Change’s (IPCC) emissions scenarios.

Kilometer-Scale Climate Models: Prospects and Challenges

AbstractCurrently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limited-area numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

Causal networks for climate model evaluation and constrained projections

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections.

Uncertainties in climate change projections covered by the ISIMIP and CORDEX model subsets from CMIP5

Abstract. Two international projects, ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) and CORDEX (Coordinated Regional Climate Downscaling Experiment), have been established to assess the impacts of global climate change and improve our understanding of regional climate respectively. Model selection from the GCMs (general circulation models) within CMIP5 (fifth phase of the Coupled Model Intercomparison Project) was conducted using the different approaches for each project: one is a globally consistent model subset used in ISIMIP and the other is a region-specific model subset for each region of interest used in CORDEX. We evaluated the ability to reproduce the regional climatological state by comparing the subsets with the full set of CMIP5 multimodel ensemble. We also investigated how well the subsets captured the uncertainty in the climate change projected by the full set, to increase credibility for the scientific outcomes from each project. The spreads of the biases and Taylor's skill scores from the ISIMIP and CORDEX subsets are smaller than that from the full set for the regional means of surface air temperature and precipitation. However, the ISIMIP and CORDEX subsets show the larger spread than high-performance models from the full set, despite using a small number of models in ISIMIP and CORDEX. It was shown that better subsets exist that would have smaller biases and/or higher scores than the current subset. The ISIMIP subset captures the uncertainty range of the regional mean of temperature change projections by the full set better than the CORDEX subsets in 10 of 14 terrestrial regions worldwide. Compared with 10 000 randomly selected subset samples, the CORDEX subset shows low coverage of the uncertainty for the temperature change projections in some regions, and the ISIMIP subset shows high coverage in all regions. On the other hand, for the precipitation change projections, the CORDEX subsets show lower coverage in half of the regions than the randomly selected subsets, but tend to cover the uncertainty wider than the ISIMIP subset. In the regions where CORDEX used nine models or more, good coverage (>50 %) is evident for the projections of both temperature and precipitation. The globally consistent model subset used in ISIMIP could have difficulty in capturing uncertainties in the regional precipitation change projections, whereas it widely covers uncertainties in the temperature change projections. The region-specific model subset, like CORDEX, can cover the uncertainties in both temperature and precipitation changes well compared to the global common subset, but a large number of models is needed. By changing the number of models from the current ensemble members to at least nine members, high coverage for both uncertainties can be also obtained in the other regions, and this information would help model selection in the next generations.

Learning about climate change uncertainty enables flexible water infrastructure planning

Water resources planning requires decision-making about infrastructure development under uncertainty in future regional climate conditions. However, uncertainty in climate change projections will evolve over the 100-year lifetime of a dam as new climate observations become available. Flexible strategies in which infrastructure is proactively designed to be changed in the future have the potential to meet water supply needs without expensive over-building. Evaluating tradeoffs between flexible and traditional static planning approaches requires extension of current paradigms for planning under climate change uncertainty which do not assess opportunities to reduce uncertainty in the future. We develop a new planning framework that assesses the potential to learn about regional climate change over time and therefore evaluates the appropriateness of flexible approaches today. We demonstrate it on a reservoir planning problem in Mombasa, Kenya. This approach identifies opportunities to reliably use incremental approaches, enabling adaptation investments to reach more vulnerable communities with fewer resources.