Uncertainty In Climate Projections Research Articles

Nitrogen and water availability control plant carbon storage with warming

Plants may slow global warming through enhanced growth, because increased levels of photosynthesis stimulate the land carbon (C) sink. However, how climate warming affects plant C storage globally and key drivers determining the response of plant C storage to climate warming remains unclear, causing uncertainty in climate projections. We performed a comprehensive meta-analysis, compiling 393 observations from 99 warming studies to examine the global patterns of plant C storage responses to climate warming and explore the key drivers. Warming significantly increased total biomass (+8.4 %), aboveground biomass (+12.6 %) and belowground biomass (+10.1 %). The effect of experimental warming on plant biomass was best explained by the availability of soil nitrogen (N) and water. Across the entire dataset, warming-induced changes in total, aboveground and belowground biomass all positively correlated with soil C:N ratio, an indicator of soil N availability. In addition, warming stimulated plant biomass more strongly in humid than in dry ecosystems, and warming tended to decrease root:shoot ratios at high soil C:N ratios. Together, these results suggest dual controls of warming effects on plant C storage; warming increases plant growth in ecosystems where N is limiting plant growth, but it reduces plant growth where water availability is limiting plant growth.

Recommendations for quantifying and reducing uncertainty in climate projections of species distributions.

Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change-rather than accurately predict specific outcomes-it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling. The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change.

Soil moisture-atmosphere feedback dominates land N2 O nitrification emissions and denitrification reduction.

Soil moisture (SM) is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that SM-atmosphere feedbacks dominate the changes in land carbon fluxes. However, the influence of SM-atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. To fill this gap, we used in situ observation coupled with gridded and remote sensing data to analyze N2 O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N2 O fluxes, as the result of SM-atmosphere feedback impact on N fluxes. We found that SM-temperature feedback dominates land N2 O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces SM, which increases the dominant N2 O production nitrifier (containing amoA AOB gene) and decreases the N2 O consumption denitrifier (containing the nosZ gene), consequently will potential increasing N2 O emissions. However, we find that the spatial variations of soil-water availability as a result of the nonlinear response of SM to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N2 O emissions. Our data-driven assessment deepens the understanding of the impact of SM-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between SM and atmospheric temperature when estimating the response of the N2 O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.

CMIP5 and CMIP6 Model Projection Comparison for Hydrological Impacts Over North America

AbstractA warmer climate impacts streamflows and these changes need to be quantified to assess future risk, vulnerability, and to implement efficient adaptation measures. The climate simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which have been the basis of most such assessments over the past decade, are being gradually superseded by the more recent Coupled Model Intercomparison Project Phase 6 (CMIP6). Our study portrays the added value of the CMIP6 ensemble over CMIP5 in a first North America wide comparison using 3,107 catchments. Results show a reduced spread of the CMIP6 ensemble compared to the CMIP5 ensemble for temperature and precipitation projections. In terms of flow indicators, the CMIP6 driven hydrological projections result in a smaller spread of future mean and high flow values, except for mountainous areas. Overall, we assess that the CMIP6 ensemble provides a narrower band of uncertainty of future climate projections, bringing more confidence for hydrological impact studies.

Observational Constraints on Southern Ocean Cloud-Phase Feedback

Abstract Shortwave radiative feedbacks from Southern Ocean clouds are a major source of uncertainty in climate projections. Much of this uncertainty arises from changes in cloud scattering properties and lifetimes that are caused by changes in cloud thermodynamic phase. Here we use satellite observations to infer the scattering component of the cloud-phase feedback mechanism and determine its relative importance by comparing it with an estimate of the overall temperature-driven cloud feedback. The overall feedback is dominated by an optical thinning of low-level clouds. In contrast, the scattering component of cloud-phase feedback is an order of magnitude smaller and is primarily confined to free-tropospheric clouds. The small magnitude of this feedback component is a consequence of counteracting changes in albedo from cloud optical thickening and enhanced forward scattering by cloud particles. These results indicate that shortwave cloud feedback is likely positive over the Southern Ocean and that changes in cloud scattering properties arising from phase changes make a small contribution to the overall feedback. The feedback constraints shift the projected 66% confidence range for the global equilibrium temperature response to doubling atmospheric CO2 by about +0.1 K relative to a recent consensus estimate of cloud feedback. Significance Statement Understanding how clouds respond to global warming is a key challenge of climate science. One particularly uncertain aspect of the cloud response involves a conversion of ice particles to liquid droplets in extratropical clouds. Here we use satellite data to infer how cloud-phase conversions affect climate by changing cloud albedo. We find that ice-to-liquid conversions increase cloud optical thickness and shift the scattering angles of cloud particles toward the forward direction. These changes in optical properties have offsetting effects on cloud albedo. This finding provides new insight about how changes in cloud phase affect climate change.

Climate nonlinearities: selection, uncertainty, projections, and damages

Climate projections are uncertain; this uncertainty is costly and impedes progress on climate policy. This uncertainty is primarily parametric (what numbers do we plug into our equations?), structural (what equations do we use in the first place?), and due to internal variability (natural variability intrinsic to the climate system). The former and latter are straightforward to characterise in principle, though may be computationally intensive for complex climate models. The second is more challenging to characterise and is therefore often ignored. We developed a Bayesian approach to quantify structural uncertainty in climate projections, using the idealised energy-balance model representations of climate physics that underpin many economists’ integrated assessment models (IAMs) (and therefore their policy recommendations). We define a model selection parameter, which switches on one of a suite of proposed climate nonlinearities and multidecadal climate feedbacks. We find that a model with a temperature-dependent climate feedback is most consistent with global mean surface temperature observations, but that the sign of the temperature-dependence is opposite of what Earth system models suggest. This difference of sign is likely due to the assumption tha the recent pattern effect can be represented as a temperature dependence. Moreover, models other than the most likely one contain a majority of the posterior probability, indicating that structural uncertainty is important for climate projections. Indeed, in projections using shared socioeconomic pathways similar to current emissions reductions targets, structural uncertainty dwarfs parametric uncertainty in temperature. Consequently, structural uncertainty dominates overall non-socioeconomic uncertainty in economic projections of climate change damages, as estimated from a simple temperature-to-damages calculation. These results indicate that considering structural uncertainty is crucial for IAMs in particular, and for climate projections in general.

Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover

Aerosol–cloud interactions have a potentially large impact on climate but are poorly quantified and thus contribute a substantial and long-standing uncertainty in climate projections. The impacts derived from climate models are poorly constrained by observations because retrieving robust large-scale signals of aerosol–cloud interactions is frequently hampered by the considerable noise associated with meteorological co-variability. The 2014 Holuhraun effusive eruption in Iceland resulted in a massive aerosol plume in an otherwise near-pristine environment and thus provided an ideal natural experiment to quantify cloud responses to aerosol perturbations. Here we disentangle significant signals from the noise of meteorological co-variability using a satellite-based machine-learning approach. Our analysis shows that aerosols from the eruption increased cloud cover by approximately 10%, and this appears to be the leading cause of climate forcing, rather than cloud brightening as previously thought. We find that volcanic aerosols do brighten clouds by reducing droplet size, but this has a notably smaller radiative impact than changes in cloud fraction. These results add substantial observational constraints on the cooling impact of aerosols. Such constraints are critical for improving climate models, which still inadequately represent the complex macro-physical and microphysical impacts of aerosol–cloud interactions. Satellite-based machine-learning analysis of a diffusive volcanic eruption suggests that aerosol climate forcing is dominated by changes in cloud cover, rather than changes in cloud brightness.

Uncertainties in future climate projections and consequential impact on maize productivity

There exist a huge uncertainty cascade in projecting the future climate and in-turn using the same in assessing the impact on crop yields. To address the problem of projecting the future climate over Tamil Nadu in Southern India, 29 Global Circulation Models defined under coupled model inter comparison project (CMIP5) of IPCC were employed for understanding the range of deviations in future climate from current condition. Outputs of representative concentration pathway (RCP) 4.5 and 8.5 scenarios for mid-century were processed for impact assessment. Selected models output was forced in dynamic crop simulation model DSSAT for assessing the impact on spatial variability in maize productivity over Tamil Nadu and possible adaptation options were tailored forreducing impacts. During the Northeast Monsoon (NEM), the maximum temperature is expected to rise by 2.0 °C for RCP 4.5 and 3.0 °C for RCP 8.5, while the minimum temperature is expected to rise by 1.8 °C for RCP 4.5 and 2.4 °C for RCP 8.5 in the mid-century. Rainfall is anticipated to vary from -22.8 to 21.1% for RCP 4.5 and -31.3 to 32.6% for RCP 8.5. The magnitude of decline in yield would be more in RCP 8.5 (30.7%) over RCP 4.5 (10.6%) scenario. Impacts of future climate change could be reduced by altering the date of sowing (early sowing) to September 1st against September 15th (normal sowing) under RCP 4.5 and RCP 8.5 scenarios. Additional dose of (25%) fertilizer had positive response with yield increase up to 15% under future climatic conditions.

Assessment of Climate Models Performance and Associated Uncertainties in Rainfall Projection from CORDEX over the Eastern Nile Basin, Ethiopia

The adverse impact of climate change on different regionally important sectors such as agriculture and hydropower is a serious concern and is currently at the epicentre of global interest. Despite the extensive efforts to project the future climate and assess its potential impact, it is surrounded by uncertainties. This study aimed to assess climate models’ performance and associated uncertainties in rainfall projection over the eastern Nile basin, Ethiopia. Seventeen climate models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and their four ensemble models were evaluated in terms of their historical prediction performance (1986–2005) and future simulation skill (2006–2016) at rainfall station (point location), grid-scale (0.44° × 0.44°) and basin scale. Station-based and spatially interpolated observed rainfall data were used as a reference during climate model performance evaluation. In addition, CRU data was used as an alternative reference data to check the effect of the reference data source on the climate models evaluation process. As the results showed, climate models have a large discrepancy in their projected rainfall and hence prior evaluation of their performance is necessary. For instance, the bias in historical mean annual rainfall averaged over the basin ranges from +760 mm (wet bias) to −582 mm (dry bias). The spatial pattern correlation (r) of climate models output and observed rainfall ranges from −0.1 to 0.7. The ensemble formed with selected (performance-based) member models outperforms the widely used multi-model ensemble in most of the evaluation metrics. This showed the need for reconsidering the widely used multi-model approach in most climate model-based studies. The use of CRU data as a reference resulted in a change in the magnitude of climate model bias. To conclude, each climate model has a certain degree of uncertainty in the rainfall projection, which potentially affects the studies on climate change and its impact (e.g., on water resources). Therefore, climate-related studies have to consider uncertainties in climate projections, which will help end-users (decision-makers) at least to be aware of the potential range of deviation in the future projected outcomes of interest.

A hydro-economic analysis of end-of-century climate projections on agricultural land and water use, production, and revenues in the U.S. Northern Rockies and Great Plains

Study region,Montana, U.S.A. Study focus Creating adaptation plans for projected imbalances in the western U.S. agricultural water demand-supply system are difficult given uncertainty in climate projections. It is critical to understand the uncertainties and vulnerabilities of the regional agricultural system and hydrologic impacts of climate change adaptation. We applied a stochastic, integrated hydro-economic model that simulates land and water allocations to analyse Montana farmer adaptations to a range of projected climate conditions and the response of the hydrologic system to those adaptations. Satellite observations of crop types, productivity, water use, and land allocation were used for model calibration. A suite of climate models was employed to quantify end-of-century impacts on streamflows, water and land use, production, and net revenues.New hydrological insights for the region Simulations showed summer streamflows were influenced by a state-wide 18.2% increase in agricultural water use. Decreased summer water availability with increased demand could have far reaching impacts downstream. Land use for irrigated crops increased 1.6%, while rainfed crops decreased 6.5%, implying state-level decrease in planted area. Even with increased land and water use for irrigated crops, production decreased 0.5%, while rainfed production decreased 2.7%. Corresponding losses in net revenues totaled 1.5% and 7.2% for irrigated and rainfed crops, respectively.Results highlight vulnerabilities of semi-arid agricultural regions and can aid water managers in sustaining agriculture in these regions.

Generating reliable estimates of tropical-cyclone-induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks

Abstract. Deriving reliable estimates of design water levels and wave conditions resulting from tropical cyclones is a challenging problem of high relevance for, among other things, coastal and offshore engineering projects and risk assessment studies. Tropical cyclone geometry and wind speeds have been recorded for the past few decades only, thus resulting in poorly reliable estimates of the extremes, especially in regions characterized by a low number of past tropical cyclone events. In this paper, this challenge is overcome by using synthetic tropical cyclone tracks and wind fields generated by the open-source tool TCWiSE (Tropical Cyclone Wind Statistical Estimation Tool) to create thousands of realizations representative of 1000 years of tropical cyclone activity for the Bay of Bengal. Each of these realizations is used to force coupled storm surge and wave simulations by means of the processed-based Delft3D Flexible Mesh Suite. It is shown that the use of synthetic tracks provides reliable estimates of the statistics of the first-order hazard (i.e., wind speed) compared to the statistics derived for historical tropical cyclones. Based on estimated wind fields, second-order hazards (i.e., storm surge and waves) are computed that are generated by the first-order hazard of wind. The estimates of the extreme values derived for wind speed, wave height and storm surge are shown to converge within the 1000 years of simulated cyclone tracks. Comparing second-order hazard estimates based on historical and synthetic tracks shows that, for this case study, the use of historical tracks (a deterministic approach) leads to an underestimation of the mean computed storm surge of up to −30 %. Differences between the use of synthetic versus historical tracks are characterized by a large spatial variability along the Bay of Bengal, where regions with a lower probability of occurrence of tropical cyclones show the largest difference in predicted storm surge and wave heights. In addition, the use of historical tracks leads to much larger uncertainty bands in the estimation of both storm surges and wave heights, with confidence intervals being +80 % larger compared to those estimated by using synthetic tracks (probabilistic approach). Based on the same tropical cyclone realizations, the effect that changes in tropical cyclone frequency and intensity, possibly resulting from climate change, may have on modeled storm surge and wave heights was computed. As a proof of concept, an increase in tropical cyclone frequency of +25.6 % and wind intensity of +1.6 %, based on literature values and without accounting for uncertainties in future climate projection, was estimated to possibly result in an increase in storm surge and wave heights of +11 % and +9 %, respectively. This suggests that climate change could increase tropical-cyclone-induced coastal hazards more than just the actual increase in maximum wind speeds.

Quantifying risks avoided by limiting global warming to 1.5 or 2 °C above pre-industrial levels

The Paris Agreement aims to constrain global warming to ‘well below 2 °C’ and to ‘pursue efforts’ to limit it to 1.5 °C above pre-industrial levels. We quantify global and regional risk-related metrics associated with these levels of warming that capture climate change–related changes in exposure to water scarcity and heat stress, vector-borne disease, coastal and fluvial flooding and projected impacts on agriculture and the economy, allowing for uncertainties in regional climate projection. Risk-related metrics associated with 2 °C warming, depending on sector, are reduced by 10–44% globally if warming is further reduced to 1.5 °C. Comparing with a baseline in which warming of 3.66 °C occurs by 2100, constraining warming to 1.5 °C reduces these risk indicators globally by 32–85%, and constraining warming to 2 °C reduces them by 26–74%. In percentage terms, avoided risk is highest for fluvial flooding, drought, and heat stress, but in absolute terms risk reduction is greatest for drought. Although water stress decreases in some regions, it is often accompanied by additional exposure to flooding. The magnitude of the percentage of damage avoided is similar to that calculated for avoided global economic risk associated with these same climate change scenarios. We also identify West Africa, India and North America as hotspots of climate change risk in the future.

A Flood Risk Management Model to Identify Optimal Defence Policies in Coastal Areas Considering Uncertainties in Climate Projections

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.

Seedlot Selection Tool and Climate‐Smart Restoration Tool: Web‐based tools for sourcing seed adapted to future climates

AbstractThe Seedlot Selection Tool and Climate‐Smart Restoration Tool are web‐based tools designed to match seedlots with planting sites assuming that seedlots are adapted to the past climates in which they evolved, primarily with respect to temperature and aridity. The tools map the climatic match of seedlots with the past or projected climates of planting sites. The challenge is that future climates are a moving target, which means that seedlots must be adapted to the near‐term climates as well as the climates of the mid‐ to late‐21st century. Because climate projections are uncertain, the prudent approach is to aim for the warmest climate that may be expected while ensuring that seedlots moved from warmer to colder locales are not moved so far that they risk cold damage. Uncertainty in climate projections may be mitigated by ensuring genetic diversity through mixing seed sources and having collections from many parents per seed source. Three examples illustrate how to effectively use the web tools: (1) choosing seedlots targeting different future climates for a mid‐elevation Douglas‐fir site in the Washington Cascades, (2) finding current and future seed sources for restoration of big sagebrush after fires in the Great Basin and Snake River Plain, and (3) planning to ensure that a Douglas‐fir seed inventory includes seedlots suitable for future climates in western Oregon and Washington.

Does Model Calibration Reduce Uncertainty in Climate Projections?

Abstract Uncertainty in climate projections is large as shown by the likely uncertainty ranges in equilibrium climate sensitivity (ECS) of 2.5–4 K and in the transient climate response (TCR) of 1.4–2.2 K. Uncertainty in model projections could arise from the way in which unresolved processes are represented, the parameter values used, or the targets for model calibration. We show that, in two climate model ensembles that were objectively calibrated to minimize differences from observed large-scale atmospheric climatology, uncertainties in ECS and TCR are about 2–6 times smaller than in the CMIP5 or CMIP6 multimodel ensemble. We also find that projected uncertainties in surface temperature, precipitation, and annual extremes are relatively small. Residual uncertainty largely arises from unconstrained sea ice feedbacks. The more than 20-year-old HadAM3 standard model configuration simulates observed hemispheric-scale observations and preindustrial surface temperatures about as well as the median CMIP5 and CMIP6 ensembles while the optimized configurations simulate these better than almost all the CMIP5 and CMIP6 models. Hemispheric-scale observations and preindustrial temperatures are not systematically better simulated in CMIP6 than in CMIP5 although the CMIP6 ensemble seems to better simulate patterns of large-scale observations than the CMIP5 ensemble and the optimized HadAM3 configurations. Our results suggest that most CMIP models could be improved in their simulation of large-scale observations by systematic calibration. However, the uncertainty in climate projections (for a given scenario) likely largely arises from the choice of parameterization schemes for unresolved processes (“structural uncertainty”), with different tuning targets another possible contributor. Significance Statement Climate models represent unresolved phenomena controlled by uncertain parameters. Changes in these parameters impact how well a climate model simulates current climate and its climate projections. Multiple calibrations of a single climate model, using an objective method, to large-scale atmospheric observations are performed. These models produce very similar climate projections at both global and regional scales. An analysis that combines uncertainties in observations with simulated sensitivity to observations and climate response also has small uncertainty showing that, for this model, current observations constrain climate projections. Recently developed climate models have a broad range of abilities to simulate large-scale climate with only some improvement in their ability to simulate this despite a decade of model development.

Ship-based estimates of momentum transfer coefficient over sea ice and recommendations for its parameterization

Abstract. A major source of uncertainty in both climate projections and seasonal forecasting of sea ice is inadequate representation of surface–atmosphere exchange processes. The observations needed to improve understanding and reduce uncertainty in surface exchange parameterizations are challenging to make and rare. Here we present a large dataset of ship-based measurements of surface momentum exchange (surface drag) in the vicinity of sea ice from the Arctic Clouds in Summer Experiment (ACSE) in July–October 2014, and the Arctic Ocean 2016 experiment (AO2016) in August–September 2016. The combined dataset provides an extensive record of momentum flux over a wide range of surface conditions spanning the late summer melt and early autumn freeze-up periods, and a wide range of atmospheric stabilities. Surface exchange coefficients are estimated from in situ eddy covariance measurements. The local sea-ice fraction is determined via automated processing of imagery from ship-mounted cameras. The surface drag coefficient, CD10n, peaks at local ice fractions of 0.6–0.8, consistent with both recent aircraft-based observations and theory. Two state-of-the-art parameterizations have been tuned to our observations, with both providing excellent fits to the measurements.

Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, RS), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and RS are irreconcilable. When we estimate GPP based on RS measurements and some assumptions about RS:GPP ratios, we found the resulted global GPP values (bootstrap mean {149}_{-23}^{+29} Pg C yr−1) are significantly higher than most GPP estimates reported in the literature ({113}_{-18}^{+18} Pg C yr−1). Similarly, historical GPP estimates imply a soil respiration flux (RsGPP, bootstrap mean of {68}_{-8}^{+10} Pg C yr−1) statistically inconsistent with most published RS values ({87}_{-8}^{+9} Pg C yr−1), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global RS:GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.

An NAO-dominated mode of atmospheric circulation drives large decadal changes in wintertime surface climate and snow mass over Eurasia

The leading mode of wintertime atmospheric variability over the North Atlantic-North Eurasia sector is dominated by the North Atlantic Oscillation (NAO) and accounts for more than one third of the total variability. This study explores the influences of the leading mode on decadal climate variability of Northern Eurasia. We focus on the little-explored decadal covariations of surface air temperature (SAT), snowfall, snow water equivalent (SWE) and snow cover over the region, using extensive model output from the Coupled Model Intercomparison Project sixth phase. Recent decadal trends (−0.92σ per decade) in the leading mode identified, are found to be largely a manifestation of internal climate variability (at least two thirds from the most conservative estimate). These internally-generated decadal trends strongly contributed to recent trends in SAT, snowfall, SWE and snow cover over Eurasia. External forcings should have played a minor role over Eurasia as they usually suggest opposite decadal trends to those observed. An exception is found for snowfall and SWE in east Eurasia, for which external forcings may have driven a large part of the recent upward trends, equally as important as the NAO-dominated mode. This points to a complex interplay between internally-generated and externally-forced climate variability over Northern Eurasia. Model discrepancies are identified in reproducing the linkages between the leading mode and the Eurasian surface climate variability. The internally-generated variability of this leading mode thus represents a large source of uncertainty in future decadal climate projections over Eurasia and, due to the memory effects of snow, also in modelling springtime climate variability.

Combining global climate models using graph cuts

Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine individual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity.

Quantifying albedo susceptibility biases in shallow clouds

Abstract. The evaluation of radiative forcing associated with aerosol–cloud interactions remains a significant source of uncertainty in future climate projections. The problem is confounded by the fact that aerosol particles influence clouds locally and that averaging to larger spatial and/or temporal scales carries biases that depend on the heterogeneity and spatial correlation of the interacting fields and the nonlinearity of the responses. Mimicking commonly applied satellite data analyses for calculation of albedo susceptibility So, we quantify So aggregation biases using an ensemble of 127 large eddy simulations of marine stratocumulus. We explore the cloud field properties that control this spatial aggregation bias and quantify the bias for a large range of shallow stratocumulus cloud conditions manifesting a variety of morphologies and ranges of cloud fractions. We show that So spatial aggregation biases can be on the order of hundreds of percent, depending on the methodology. Key uncertainties emanate from the typically applied adiabatic drop concentration Nd retrieval, the correlation between aerosol and cloud fields, and the extent to which averaging reduces the variance in cloud albedo Ac and Nd. So biases are more often positive than negative and are highly correlated with biases in the liquid water path adjustment. Temporal aggregation biases are shown to offset spatial aggregation biases. Both spatial and temporal biases have significant implications for observationally based assessments of aerosol indirect effects and our inferences of underlying aerosol–cloud–radiation effects.