Emergent Constraints Research Articles

Emergency constraint-based CMIP6 predictions for future droughts on the Mongolian Plateau

Accurate predictions of future drought changes in the context of warming play an important role for both human social and ecological environments. This study aims to predict future droughts on the Mongolian Plateau using the latest Coupled Model Comparison Project dataset. First, the historical and future relationships of 17 Global Climate Models (GCMs) were constrained using the emergent constraints method to establish new predictions for future droughts under SSP2-4.5 and SSP5-8.5 scenarios. Based on two scenarios, spatial and temporal variations of future droughts on the Mongolian Plateau were analyzed. Future droughts will be worse compared to historical periods. In the SSP2-4.5 scenario, the future drought intensity shows an increasing trend, the frequency of drought gradually increases, and the spatial variation of both decreases gradually from the southwest to the northeast. Under the SSP5-8.5 scenario, the intensity and frequency of droughts increases from 2020 to 2060; from 2061 to 2100, the intensity and frequency of droughts show a decreasing trend. The spatial variation is uniform, with high values in the southwest and low values in the northeast.

Modelling global mesozooplankton biomass using machine learning

Mesozooplankton are a crucial link between primary producers and higher trophic levels and play a vital role in marine food webs, biological carbon pumps, and sustaining fishery resources. However, the global distribution of mesozooplankton biomass and the relevant controlling mechanisms remain elusive. We compared four machine learning algorithms (Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine) to model the spatiotemporal distributions of global mesozooplankton biomass. These algorithms were trained on a compiled dataset of published mesozooplankton biomass observations with corresponding environmental predictors from contemporaneous satellite observations (temperature, chlorophyll, salinity, and mixed layer depth). We found that Random Forest achieved the best predictive accuracy with R2 and RMSE (Root Mean Standard Error) of 0.57 and 0.39, respectively. Also, the global distribution of mesozooplankton biomass predicted by the Random Forest model was more consistent with the observational data than other models. We used the Random Forest model to create a global map of mesozooplankton biomass which serves as a reference for validating process-based ecosystem models. The model outputs confirm that environmental factors, especially surface Chl a, a proxy for prey availability, significantly correlate with the spatiotemporal distribution of mesozooplankton biomass. The scaling relationship between the mesozooplankton biomass and Chl a can be used as an emergent constraint for model validation and development. Moreover, our model predicts that the global mesozooplankton biomass will decrease by 3% by the end of this century under the “business-as-usual” scenarios, potentially reducing fishery production and carbon sequestration. Our study contributes to predicting global mesozooplankton biomass and provides deep insights into the underlying environmental impacts on the distribution of mesozooplankton biomass.

Emergent constraints on future Amazon climate change-induced carbon loss using past global warming trends

Reducing uncertainty in the response of the Amazon rainforest, a vital component of the Earth system, to future climate change is crucial for refining climate projections. Here we demonstrate an emergent constraint (EC) on the future response of the Amazon carbon cycle to climate change across CMIP6 Earth system models. Models that overestimate past global warming trends, tend to estimate hotter and drier future Amazon conditions, driven by northward shifts of the intertropical convergence zone over the Atlantic Ocean, causing greater Amazon carbon loss. The proposed EC changes the mean CMIP6 Amazon climate-induced carbon loss estimate (excluding CO2 fertilisation and land-use change impacts) from −0.27 (−0.59–0.05) to −0.16 (−0.42–0.10) GtC year−1 at 4.4 °C warming level, reducing the variance by 34%. This study implies that climate-induced carbon loss in the Amazon rainforest by 2100 is less than thought and that past global temperature trends can be used to refine regional carbon cycle projections.

Observation-constrained projections reveal longer-than-expected dry spells

Climate models indicate that dry extremes will be exacerbated in many regions of the world1,2. However, confidence in the magnitude and timing of these projected changes remains low3,4, leaving societies largely unprepared5,6. Here we show that constraining model projections with observations using a newly proposed emergent constraint (EC) reduces the uncertainty in predictions of a core drought indicator, the longest annual dry spell (LAD), by 10–26% globally. Our EC-corrected projections reveal that the increase in LAD will be 42–44% greater, on average, than ‘mid-range’ or ‘high-end’ future forcing scenarios currently indicate. These results imply that by the end of this century, the global mean land-only LAD could be 10 days longer than currently expected. Using two generations of climate models, we further uncover global regions for which historical LAD biases affect the magnitude of projected LAD increases, and we explore the role of land–atmosphere feedbacks therein. Our findings reveal regions with potentially higher- and earlier-than-expected drought risks for societies and ecosystems, and they point to possible mechanisms underlying the biases in the current generation of climate models.

Emergent constraints on historical and future global gross primary productivity.

Terrestrial gross primary productivity (GPP) is the largest carbon flux in the global carbon cycle and plays a crucial role in terrestrial carbon sequestration. However, historical and future global GPP estimates still vary markedly. In this study, we reduced uncertainties in global GPP estimates by employing an innovative emergent constraint method on remote sensing-based GPP datasets (RS-GPP), using ground-based estimates of GPP from flux towers as the observational constraint. Using this approach, the global GPP in 2001-2014 was estimated to be 126.8 ± 6.4 PgC year-1, compared to the original RS-GPP ensemble mean of 120.9 ± 10.6 PgC year-1, which reduced the uncertainty range by 39.6%. Independent space- and time-based (different latitudinal zones, different vegetation types, and individual year) constraints further confirmed the robustness of the global GPP estimate. Building on these insights, we extended our constraints to project global GPP estimates in 2081-2100 under various Shared Socioeconomic Pathway (SSP) scenarios: SSP126 (140.6 ± 9.3 PgC year-1), SSP245 (153.5 ± 13.4 PgC year-1), SSP370 (170.7 ± 16.9 PgC year-1), and SSP585 (194.1 ± 23.2 PgC year-1). These findings have important implications for understanding and projecting climate change, helping to develop more effective climate policies and carbon reduction strategies.

Testing the assumptions in emergent constraints: why does the “emergent constraint on equilibrium climate sensitivity from global temperature variability” work for CMIP5 and not CMIP6?

Testing the assumptions in emergent constraints: why does the “emergent constraint on equilibrium climate sensitivity from global temperature variability” work for CMIP5 and not CMIP6?

Qualifying uncertainty of precipitation projections over China: mitigating uncertainty with emergent constraints

Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.

No constraint on long-term tropical land carbon-climate feedback uncertainties from interannual variability

Unraveling drivers of the interannual variability of tropical land carbon cycle is critical for understanding land carbon-climate feedbacks. Here we utilize two generations of factorial model experiments to show that interannual variability of tropical land carbon uptake under both present and future climate is consistently dominated by terrestrial water availability variations in Earth system models. The magnitude of this interannual sensitivity of tropical land carbon uptake to water availability variations under future climate shows a large spread across the latest 16 models (2.3 ± 1.5 PgC/yr/Tt H2O), which is constrained to 1.3 ± 0.8 PgC/yr/Tt H2O using observations and the emergent constraint methodology. However, the long-term tropical land carbon-climate feedback uncertainties in the latest models can no longer be directly constrained by interannual variability compared with previous models, given that additional important processes are not well reflected in interannual variability but could determine long-term land carbon storage. Our results highlight the limited implication of interannual variability for long-term tropical land carbon-climate feedbacks and help isolate remaining uncertainties with respect to water limitations on tropical land carbon sink in Earth system models.

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

AbstractGlobal climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

Emergent constraints on the future East Asian winter surface air temperature changes

In East Asia, the climate variability in boreal winter is dominated by the East Asian winter monsoon, one of the most energetic monsoon systems that can lead to disasters. The key variable, the East Asian winter surface air temperature (SAT), has significantly changed over the past century and has substantially impacted agriculture, ecosystems, economics, and public health. However, its projections are limited by considerable uncertainties. Here, we identify the first leading mode that explains almost 29.6% of the inter-model spread in future SAT change. Our research delves into the evolution of present-day biases under future scenarios and their consequential impact on the SAT. Models with stronger western currents’ heat transport in the North Pacific exhibit a warmer North Pacific at mid-latitudes during historical periods. Additionally, these models consistently demonstrate stronger western currents in the future, contributing to the amplified warming of the western North Pacific, thereby warming Eurasia via the weakened trough and subtropical jet through barotropic responses to the warm North Pacific. Incorporating observational sea surface temperature constraints reduces uncertainties by 9.40%, revealing a more reliable SAT change pattern by the end of the 21st century.

Emergent constraint on oxygenation of the upper South Eastern Pacific oxygen minimum zone in the twenty-first century

As a consequence of on-going global warming, the ocean is losing oxygen, which has implications not only in terms of marine resources management and food supply but also in terms of the potentially important feedback on the global carbon cycle and climate. Of particular scrutiny are the extended zones of already low levels of oxygen called the oxygen minimum zones (OMZs) embedded in the subsurface waters of the productive Eastern Boundary Upwelling Systems (EBUS). These OMZs are currently diversely simulated by state-of-the-art Earth System Models (ESM) hampering a reliable projection of ocean deoxygenation on marine ecosystem services in these regions. Here we focus on the most emblematic EBUS OMZs of the planet, that of the South Eastern Pacific (SEP), which is under the direct influence of the El Niño Southern Oscillation (ENSO), the main climate mode on interannual timescales at global scale. We show that, despite the low consensus among ESM long-term projections of oxygen levels, the sensitivity of the depth of the upper margin (oxycline) of the SEP OMZ to El Niño events in an ensemble of ESMs can be used as a predictor of its long-term trend, which establishes an emergent constraint for the SEP OMZ. Because the oxycline along the coast of Peru and Chile deepens during El Niño events, the upper bound of the SEP OMZ is thus likely to deepen in the future climate, therefore oxygenating the SEP OMZ. This has implications not only for understanding the nitrogen and carbon cycles at global scale but also for designing adaptation strategies for regional upper-ocean ecosystem services.

CMIP6 ESMs overestimate greening and the photosynthesis trends in Dryland East Asia

The Dryland East Asia (DEA) is one of the largest inland arid regions, and vegetation is very sensitive to climate change. The complex environment in DEA with defects of modeling construction make it difficult to simulate and predict changes in vegetation structure and productivity. Here, we use the emergent constraint (EC) method to constrain the future interannual leaf area index (LAI) and gross primary productivity (GPP) trends in DEA, under four scenarios of the latest Sixth Coupled Model Intercomparison Project (CMIP6) model ensemble. LAI and GPP increase in all scenarios in the near term (2015–2050), with continued growth in SSP370 and SSP585 and stasis in SSP126 and SSP245 in the far term (2051–2100). However, after building effective EC relationships, the constrained increasing trends of LAI (GPP) are reduced by 43.5 %–53.9 % (30.5 %–50.0 %) compared with the uncertainties of the original ensemble, which are reduced by 10.0 %–45.7 % (4.6 %–34.3 %). We also extend the EC in moving windows and grid cells, further strengthening the robustness of the constraints, especially by illustrating spatial sources of these emergent relationships. Overestimations of LAI and GPP trends suggest that current CMIP6 models may be insufficient to capture the complex relationships between climate change and vegetation dynamics in DEA; however, these models can be adjusted based on established emergent relationships.

Southern Ocean phytoplankton under climate change: a shifting balance of bottom-up and top-down control

Abstract. Phytoplankton form the base of the marine food web by transforming CO2 into organic carbon via photosynthesis. Despite the importance of phytoplankton for marine ecosystems and global carbon cycling, projections of phytoplankton biomass in response to climate change differ strongly across Earth system models, illustrating uncertainty in our understanding of the underlying processes. Differences are especially large in the Southern Ocean, a region that is notoriously difficult to represent in models. Here, we argue that total (depth-integrated) phytoplankton biomass in the Southern Ocean is projected to largely remain unchanged under climate change by the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble because of a shifting balance of bottom-up and top-down processes driven by a shoaling mixed-layer depth. A shallower mixed layer is projected on average to improve growth conditions, consequently weaken bottom-up control, and confine phytoplankton closer to the surface. An increase in the phytoplankton concentration promotes zooplankton grazing efficiency, thus intensifying top-down control. However, large differences across the model ensemble exist, with some models simulating a decrease in surface phytoplankton concentrations. To reduce uncertainties in projections of surface phytoplankton concentrations, we employ an emergent constraint approach using the observed sensitivity of surface chlorophyll concentration, taken as an observable proxy for phytoplankton, to seasonal changes in the mixed-layer depth as an indicator for future changes in surface phytoplankton concentrations. The emergent constraint reduces uncertainties in surface phytoplankton concentration projections by around one-third and increases confidence that surface phytoplankton concentrations will indeed rise due to shoaling mixed layers under global warming, thus favouring intensified top-down control. Overall, our results suggest that while changes in bottom-up conditions stimulate enhanced growth, intensified top-down control opposes an increase in phytoplankton and becomes increasingly important for the phytoplankton response to climate change in the Southern Ocean.

Emergent Constraints on Future Projections of Tibetan Plateau Warming in Winter

AbstractThe Tibetan Plateau (TP) is an area highly sensitive to climate change and is warming faster than the global average. The TP temperature change has a significant impact on the local ecological environment and the downstream weather and climate. The TP will undoubtedly warm in the future, but the warming extent is uncertain. Using the Coupled Model Inter‐comparison Project Phase 6 multi‐model ensemble, we found that models simulating smaller TP temperature increases in recent decades tend to project weaker warming in the future. This relationship is driven by the simulation of snowmelt response to greenhouse gas increases, as snow‐related albedo feedback dominates the TP temperature changes in both historical and future periods. Based on a two‐step emergent constraint approach, the rectified TP warming magnitude increases by about 0.3°C compared to the unconstrained result under both the medium and high emission scenarios, and the inter‐model uncertainty is reduced by about 60%.

Emergent constraint on the projected central equatorial Pacific warming and northwestern Pacific monsoon trough change

The northwestern Pacific monsoon trough (NWPMT) deeply impacts socio-economic development and human security over East Asia by supplying moisture to the summer monsoon rainfall and modulating tropical cyclone activities. However, considerable inter-model spreads in the coupled model inter-comparison project phase 6 models make the future projection of the NWPMT less reliable. Here, we find that the inter-model spread of the NWPMT change is significantly correlated with the central equatorial Pacific sea surface temperature change, and mainly determined by the equatorial thermocline sharpness in the historical simulations. According to the emergent constraint method, the central equatorial Pacific SST would warm up about 6% slower than the multi-model mean with 56% uncertainty reduced. Correspondingly, the NWPMT would slacken westward with 36% uncertainty reduced. Results here emphasize the importance of examining and reducing systematic model biases in simulating thermocline sharpness that have been overlooked in past literatures, before achieving more reliable future projections.

Future projections of meteorological, agricultural and hydrological droughts in China using the emergent constraint

Study regionChina. Study focusIn the context of global warming, droughts occur more frequently and have caused great losses to human society. Therefore, understanding the potential changes in future droughts under climate change is of great scientific significance for disaster prevention and mitigation. In this paper, combining with climate models from CMIP6, the emergent constraint and the skill scoring methods are used to analyze the characteristics of meteorological, agricultural and hydrological droughts in China under four socioeconomic scenarios in the mid and late 21st century. New hydrological insights for the regionIn the mid-21st century, there are more frequent meteorological, agricultural and hydrological droughts in northern China. In the late 21st century, sustained and more intense droughts are more likely to occur in China than in the mid-21st century. Additionally, the relationship between meteorological drought and agricultural/hydrological drought is strengthened to varying degrees in the future. From the perspective of extreme drought events, the probabilities of extreme drought events with long duration and high severity increase significantly in Northwest and Central China under each scenarios. In a higher socioeconomic scenario, drought is suppressed in the mid 21st century, but is intensified in the late 21st century. These findings are of great significance for drought monitoring under climate change and can provide a basis for developing measures to respond to drought.

An emergent constraint on the thermal sensitivity of photosynthesis and greenness in the high latitude northern forests

Despite the general consensus that the warming over the high latitudes northern forests (HLNF) has led to enhanced photosynthetic activity and contributed to the greening trend, isolating the impact of temperature increase on photosynthesis and greenness has been difficult due to the concurring influence of the CO2 fertilization effect. Here, using an ensemble of simulations from biogeochemical models that have contributed to the Trends in Net Land Atmosphere Carbon Exchange project (TRENDY), we identify an emergent relationship between the simulation of the climate-driven temporal changes in both gross primary productivity (GPP) and greenness (Leaf Area Index, LAI) and the model’s spatial sensitivity of these quantities to growing-season (GS) temperature. Combined with spatially-resolved observations of LAI and GPP, we estimate that GS-LAI and GS-GPP increase by 17.0 ± 2.4% and 24.0 ± 3.0% per degree of warming, respectively. The observationally-derived sensitivities of LAI and GPP to temperature are about 40% and 71% higher, respectively, than the mean of the ensemble of simulations from TRENDY, primarily due to the model underestimation of the sensitivity of light use efficiency to temperature. We estimate that the regional mean GS-GPP increased 28.2 ± 5.1% between 1983–1986 and 2013–2016, much larger than the 5.8 ± 1.4% increase from the CO2 fertilization effect implied by Wenzel et al. This suggests that warming, not CO2 fertilization, is primarily responsible for the observed dramatic changes in the HLNF biosphere over the last century.

Emergent Constraint on Projection of the North Pacific Monsoon Trough and Its Implications for Typhoon Activity Using CMIP6 Models

AbstractThe North Pacific monsoon trough (NPMT) is an imperative large‐scale circulation pattern influencing tropical cyclone (TC) activity in the western North Pacific (WNP), thus its future change has great implications for the WNP TC activity. Here future change in the NPMT and its uncertainty are examined by using 35 climate models from Phase six of Coupled Model Intercomparison Project (CMIP6). Due to the El Niño‐like sea surface temperature (SST) pattern, the multi‐model ensemble (MME) mean projects an eastward extension of the NPMT in the latter half of the 21st century. However, considerable inter‐model uncertainty exists in the projection, which is associated with the diversity in the zonal SST gradient across the tropical Pacific. This diversity in the projected SST is largely rooted in the simulated precipitation biases in the tropical Pacific in the historical experiments of CMIP6 models, which can modulate future Pacific SST distribution through the shortwave‐SST feedback. This linkage between biases in historical climate and future climate allows us to constrain the projection by using observational data sets. Emergent constraint using observational precipitation data set reduces the projection uncertainty by 48% and projects an eastward extension of the north Pacific NPMT by 6.2°. This eastward extension of the NPMT indicates an eastward migration of TC genesis location, and elongated TC track and thus strengthened TC intensity, suggesting an increased TC‐related disaster for residents near the WNP.

Emergent constraints on carbon budgets as a function of global warming

Earth System Models (ESMs) continue to diagnose a wide range of carbon budgets for each level of global warming. Here, we present emergent constraints on the carbon budget as a function of global warming, which combine the available ESM historical simulations and future projections for a range of scenarios, with observational estimates of global warming and anthropogenic CO2 emissions to the present day. We estimate mean and likely ranges for cumulative carbon budgets for the Paris targets of 1.5 °C and 2 °C of global warming of 812 [691, 933] PgC and 1048 [881, 1216] PgC, which are more than 10% larger than the ensemble mean values from the CMIP6 models. The linearity between cumulative emissions and global warming is found to be maintained at least until 4 °C, and is consistent with an effective Transient Climate Response to Emissions (eTCRE) of 2.1 [1.8, 2.6] °C/1000PgC, from a global warming of 1.2 °C onwards.

Projections of the North Atlantic warming hole can be constrained using ocean surface density as an emergent constraint

There are large uncertainties in the projections of the future of the warming hole, which is defined as the cooling trend in the subpolar North Atlantic Ocean despite of global warming. Here, we found that the uncertainty in the future changes of the warming hole is mainly due to model bias rather than either natural variability or climate change scenario. Observations and model results constrain the future warming hole intensity in terms of its relationship with the present-day surface density in the subpolar North Atlantic. Models with a low present-day surface density tend to project a weaker warming hole intensity (i.e., a stronger temperature increase) due to a suppressed oceanic deep convection in the future than models with a high surface density. This relationship was robust across all scenarios and was used to reduce the uncertainty of the future warming hole intensity by 39% in the high emissions scenario.