Earth System Model Output Research Articles

Assessing CMIP6 models in simulating meteo-oceanographic variability on Spanish continental coasts

The ocean is a key player in the Earth's climate, absorbing heat and carbon dioxide from the atmosphere. The ocean's temperature and salinity are influenced by climate change, which can significantly impact marine ecosystems. Reliable data sets of atmospheric and oceanographic parameters are of special interest in coastal productive areas to adequately monitor their variability at regional and local levels. It is therefore essential to continue monitoring and studying the ocean to develop effective mitigation and adaptation strategies. In this context, the main aim of this study is to identify the Earth System Models (ESMs) from the Coupled Model Intercomparison Project 6 (CMIP6) that best capture the variability of water temperature, salinity, and wind speed along the continental Spanish coasts, using them to estimate future impacts in these regions. To achieve this, a multifaceted approach is used, encompassing a historical (2000−2014) assessment comparing ESM outputs to in situ observations from Puertos del Estado (PdE) oceanographic buoys, an examination of the present period (2015−2022) under three IPCC scenarios (SSP1−2.6, SSP2−4.5, and SSP5−8.5), and a projection of future (2023−2100) trends using the same emission scenarios. Results showed that the ESMs from CMIP6 can reproduce the historical patterns of meteo-oceanographic properties, rendering them valuable tools for climate change studies. In the future (2100), considering the most pessimistic scenario (SSP5−8.5), the water temperature may increase by 2.8°C, salinity may decrease by -1.6, and wind speed may decrease by -0.4 m·s−1. These projected changes can significantly impact the Spanish coasts, jeopardizing the growth, reproduction, survival, abundance, and distribution of some marine species.

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

AbstractClimate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns.

Improving Earth System Model Selection Methodologies for Projecting Hydroclimatic Change: Case Study in the Pacific Northwest

AbstractThe rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments.

Global Vegetation‐Temperature Sensitivity and Its Driving Forces in the 21st Century

AbstractIt has been projected that climatic warming will contribute to vegetation productivity variability at the global scale. With a continued warming, to what extent and where the vegetation productivity is most affected by warming has still not been adequately quantified. Herein, based on 11 earth system model outputs, we predict the characteristics of vegetation‐temperature sensitivity (Svpt, defined as higher/lower temperature produce more/less vegetation) changes under different CO2 emission scenarios and various vegetation types, further assessing the relationship of the Svpt to socio‐ecosystems. At the end of the 21st century, the area proportion with the global temperature increases >2°C under the SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios are 3%, 40%, 97%, and 99%, respectively. The largest contribution to the global terrestrial gross primary productivity growth is at low latitudes. 33%–63% of global terrestrial ecosystems show a significantly negative trend in the Svpt, indicating the weakened promotion effect of warming on vegetation growth. In particular, in 2060, there will be a clear reversal of the trend from carbon sink to carbon source under the SSP3‐7.0 scenario, spatially distributed mainly in the Amazon rainforest, tropical Africa, and southern North America. Precipitation is also an important factor affecting the Svpt change, and there is an inverted U‐shaped relationship between them. Precipitation thresholds are higher under the high emission scenario when Svpt reaches its highest value. Moreover, socio‐demographic pressures in places like Central Africa and East Africa will offset the promotion of vegetation growth by warming; in the future, these countries should develop appropriate population and land management strategies to achieve socio‐ecosystems sustainable development.

More Frequent Abrupt Marine Environmental Changes Expected

AbstractWe quantify an elevated occurrence of abrupt changes in ocean environmental conditions under human‐induced climate forcing using Earth system model output through a novel analysis method that compares the temporal evolution of the forcings applied with the development of local ocean state changes for temperature, oxygen concentration, and carbonate ion concentration. Through a multi‐centennial Earth system model experiment, we show that such an increase is not fully reversible after excess greenhouse gas emissions go back to zero. The increase in occurrence of regional abrupt changes in marine environmental conditions has not yet been accounted for adequately in climate impact analyses that usually associate ecosystem shifts large‐scale variability or extreme events. Estimates for remaining greenhouse gas emission targets need thus to be more conservative.

Evaluation of Different Bias Correction Methods for Dynamical Downscaled Future Projections of the California Current Upwelling System

AbstractBiases in global Earth System Models (ESMs) are an important source of errors when used to obtain boundary conditions for regional models. Here we examine historical and future conditions in the California Current System (CCS) using three different methods to force the regional model: (a) interpolation of ESM output to the regional grid with no bias correction; (b) a “seasonally‐varying” delta method that obtains a season‐dependent mean climate change signal from the ESM for a 30‐year future period; and (c) a “time‐varying” delta method that includes the interannual variability of the ESM over the 1980–2100 period. To compare these methods, we use a high‐resolution (0.1°) physical‐biogeochemical regional model to dynamically downscale an ESM projection under the RCP8.5 emission scenario. Using different downscaling methods, the sign of future changes agrees for most of the physical and ecosystem variables, but the spatial patterns and magnitudes of these changes differ, with the seasonal‐ and time‐varying delta simulations showing more similar changes. Not correcting the ESM forcing leads to amplification of biases in some ecosystem variables as well as misrepresentation of the California Undercurrent and CCS source waters. In the non‐bias corrected and time‐varying delta simulations, most of the ecosystem variables inherit trends and decadal variability from the ESM, while in the seasonally‐varying delta simulation the future variability reflects the observed historical variability (1980–2010). Our results demonstrate that bias correcting the forcing prior to downscaling improves historical simulations, and that the bias correction method may impact the spatial and temporal variability of the future projections.

GOBAI-O2: temporally and spatially resolved fields of ocean interior dissolved oxygen over nearly 2 decades

Abstract. For about 2 decades, oceanographers have been installing oxygen sensors on Argo profiling floats to be deployed throughout the world ocean, with the stated objective of better constraining trends and variability in the ocean's inventory of oxygen. Until now, measurements from these Argo-float-mounted oxygen sensors have been mainly used for localized process studies on air–sea oxygen exchange, upper-ocean primary production, biological pump efficiency, and oxygen minimum zone dynamics. Here, we present a new four-dimensional gridded product of ocean interior oxygen, derived via machine learning algorithms trained on dissolved oxygen observations from Argo-float-mounted sensors and discrete measurements from ship-based surveys and applied to temperature and salinity fields constructed from the global Argo array. The data product is called GOBAI-O2, which stands for Gridded Ocean Biogeochemistry from Artificial Intelligence – Oxygen (Sharp et al., 2022; https://doi.org/10.25921/z72m-yz67); it covers 86 % of the global ocean area on a 1∘ × 1∘ (latitude × longitude) grid, spans the years 2004–2022 with a monthly resolution, and extends from the ocean surface to a depth of 2 km on 58 levels. Two types of machine learning algorithms – random forest regressions and feed-forward neural networks – are used in the development of GOBAI-O2, and the performance of those algorithms is assessed using real observations and simulated observations from Earth system model output. Machine learning represents a relatively new method for gap filling ocean interior biogeochemical observations and should be explored along with statistical and interpolation-based techniques. GOBAI-O2 is evaluated through comparisons to the oxygen climatology from the World Ocean Atlas, the mapped oxygen product from the Global Ocean Data Analysis Project and to direct observations from large-scale hydrographic research cruises. Finally, potential uses for GOBAI-O2 are demonstrated by presenting average oxygen fields on isobaric and isopycnal surfaces, average oxygen fields across vertical–meridional sections, climatological seasonal cycles of oxygen averaged over different pressure layers, and globally integrated time series of oxygen. GOBAI-O2 indicates a declining trend in the oxygen inventory in the upper 2 km of the global ocean of 0.79 ± 0.04 % per decade between 2004 and 2022.

Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models

AbstractThe accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross‐scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State‐of‐the‐art bias correction methods only address errors in the simulated frequency distributions locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a postprocessing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state‐of‐the‐art, CMIP6‐class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold‐standard bias‐adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.

Dynamically downscaled projection of urban outdoor thermal stress and indoor space cooling during future extreme heat

While projected urban air temperatures under climate change and urbanization have received attention, projections of pedestrian thermal stress are scarce and usually produced by statistical downscaling. In this study, we present and evaluate a dynamical downscaling methodology to assess urban outdoor thermal exposure. We dynamically downscale Earth system model output with a mesoscale weather, urban canopy and building energy model (WRF-BEP-BEM), then use the downscaled output as boundary conditions for a microscale model (TUF-Pedestrian) to determine metre-scale mean radiant temperature. Using this methodology, we assess outdoor heat exposure and air conditioning loading during typical heatwaves at the start and end of century in Phoenix and Toronto.Results reveal that air conditioning loading would double in Phoenix by the end of century under an RCP 8.5 climate and urban development scenario; and it may double in Toronto even without urban development. This is compounded by up to 2 additional hours/day of extreme heat stress in Phoenix (5.5 additional hours/day of strong heat stress in Toronto). Using our dynamical downscaling methodology, we find that projected climate change-induced increases in outdoor heat stress largely result from higher air temperatures (Phoenix: 69–87%; Toronto: 60–64%), with smaller contributions from increased mean radiant temperature and humidity.

TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change

Abstract. Land cover changes have been proposed to play a significant role, alongside emission reductions, in achieving the temperature goals agreed upon under the Paris Agreement. Such changes carry both global implications, pertaining to the biogeochemical effects of land cover change and thus the global carbon budget, and regional or local implications, pertaining to the biogeophysical effects arising within the immediate area of land cover change. Biogeophysical effects of land cover change are of high relevance to national policy and decision makers, and accounting for them is essential for effective deployment of land cover practices that optimise between global and regional impacts. To this end, Earth system model (ESM) outputs that isolate the biogeophysical responses of climate to land cover changes are key in informing impact assessments and supporting scenario development exercises. However, generating multiple such ESM outputs in a manner that allows comprehensive exploration of all plausible land cover scenarios is computationally untenable. This study proposes a framework to explore in an agile manner the local biogeophysical responses of climate under customised tree cover change scenarios by means of a computationally inexpensive emulator, the Tree cover change clIMate Biophysical responses EmulatoR (TIMBER) v0.1. The emulator is novel in that it solely represents the biogeophysical responses of climate to tree cover changes, and it can be used as either a standalone device or as a supplement to existing climate model emulators that represent the climate responses from greenhouse gas (GHG) or global mean temperature (GMT) forcings. We start off by modelling local minimum, mean, and maximum surface temperature responses to tree cover changes by means of a month- and Earth system model (ESM)-specific generalised additive model (GAM) trained over the whole globe; 2 m air temperature responses are then diagnosed from the modelled minimum and maximum surface temperature responses using observationally derived relationships. Such a two-step procedure accounts for the different physical representations of surface temperature responses to tree cover changes under different ESMs whilst respecting a definition of 2 m air temperature that is more consistent across ESMs and with observational datasets. In exploring new tree cover change scenarios, we employ a parametric bootstrap sampling method to generate multiple possible temperature responses, such that the parametric uncertainty within the GAM is also quantified. The output of the final emulator is demonstrated for the Shared Socioeconomic Pathway (SSP) 1-2.6 and 3-7.0 scenarios. Relevant temperature responses are identified as those displaying a clear signal in relation to their surrounding parametric uncertainty, calculated as the signal-to-noise ratio between the sample set mean and sample set variability. The emulator framework developed in this study thus provides a first step towards bridging the information gap surrounding biogeophysical implications of land cover changes, allowing for smarter land use decision making.

Carbon on the Ocean Surface: Temporal and Geographical Investigation

The sea surface nanolayer (SSnL) is enriched with lipids and proteins; working together, these macromolecules create a unique region between the atmosphere and ocean. Lipids and proteins affect the ocean’s surface chemistry, which causes surface tension suppression. Carbon enrichment is ultimately reflected in organic sea spray aerosols and wave damping mechanisms that impact micrometeorology. In the present work, we calculate and examine carbon in the SSnL with a global analysis of carbon that incorporates organic surfactant dynamics. Our carbon estimates are based on chlorophyll distributions deduced from Energy Exascale Earth System Model output data. We assume that chlorophyll can approximate phytoplankton and that phytoplankton are primary contributors to the dissolved organic carbon (DOC) pool. The model calculates the carbon concentration from phytoplankton and zooplankton concentrations, fits a Langmuir isotherm to determine fractional surface coverage, and converts from the fractional coverage to a mass using surface excess to estimate carbon in the SSnL. Organic geochemical diversity of the ocean surface is evidenced by monthly variability and global differences among carbon averages. The model is an estimation of carbon because of the limited data for carbohydrate co-adsorption and molecular behavior of the DOC and particulate organic carbon (POC) pool, but our results can be used as a benchmark throughout future works. Our results reveal large seasonal shifts across the major biomes and among regional interfacial totals. However, the total mass of ∼10–4 gigatons does not change significantly throughout the year. We propose based on our results that adsorptive equilibria control the organic content of the nanolayer. To the best of our knowledge, these calculations constitute the first example of SSnL organic carbon integrations performed on a planetary scale.

Evaluation of native Earth system model output with ESMValTool v2.6.0

Abstract. Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool needs to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as “CMORization”. While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing of “native” climate model output, i.e., operational output produced by running the climate model through the standard workflow of the corresponding modeling institute. This is achieved by an extension of ESMValTool's preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitude–longitude grid. Extensions to ESMValTool's regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids. Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; at the moment, only surface variables are available), EC-Earth3, EMAC, ICON, and IPSL-CM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added.

Projecting Future Energy Production from Operating Wind Farms in North America. Part I: Dynamical Downscaling

Abstract New simulations at 12-km grid spacing with the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF; measures of electrical power production) computed by applying a power curve to hourly wind speeds at wind turbine hub height from this simulation are also used to illustrate the pitfalls in seeking to infer changes in wind power generation directly from low-spatial-resolution and time-averaged ESM output. WRF-derived CF are evaluated using observed daily CF from operating wind farms. The spatial correlation coefficient between modeled and observed mean CF is 0.65, and the root-mean-square error is 5.4 percentage points. Output from the MPI-WRF Model chain also captures some of the seasonal variability and the probability distribution of daily CF at operating wind farms. Projections of mean annual CF (CFA) indicate no change to 2050 in the southern Great Plains and Northeast. Interannual variability of CFA increases in the Midwest, and CFA declines by up to 2 percentage points in the northern Great Plains. The probability of wind droughts (extended periods with anomalously low production) and wind bonus periods (high production) remains unchanged over most of the eastern United States. The probability of wind bonus periods exhibits some evidence of higher values over the Midwest in the 2040s, whereas the converse is true over the northern Great Plains. Significance Statement Wind energy is playing an increasingly important role in low-carbon-emission electricity generation. It is a “weather dependent” renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential. We use PP data from operating wind farms to demonstrate that regional simulations exhibit skill in capturing actual power production. Projections to the middle of this century indicate that over most of North America east of the Rocky Mountains annual expected PP is largely unchanged, as is the probability of extended periods of anomalously high or low production. Any small declines in annual PP are of much smaller magnitude than changes due to technological innovation over the last two decades.

How desertification in northern China will change under a rapidly warming climate in the near future (2021–2050)

Arid, semi-arid, and semi-humid regions (drylands) with fragile ecological balance have undergone dramatic climate change in past decades, and how the desertification will change under a continuous warming background still remain uncertain. In this study, the bias-corrected Community Earth System Model outputs from Coupled Model Intercomparison Project phase 5 were dynamically downscaled using the Weather Research and Forecasting (WRF) model, based on which the evolution trend of desertification over northern China (NC) in the past (1972–2000) and the near future period (2021–2050) under the RCP8.5 scenario were analyzed using the dune mobility index, and the impacts of climate change on the intensification or reversal of desertification over NC in the near future were explored. The results show that WRF downscaling can reproduce the desertification changes over NC in the past. The regions with a high risk of desertification are mainly located on the border of the desert and gobi. Under a rapidly warming climate in the near future, desertification will likely reverse in most regions of NC, especially for regions north of 40°N over NC. Potential evapotranspiration changes will exacerbate desertification, while precipitation changes will promote rehabilitation, and wind speed changes show obvious local impacts on desertification. The results in this study imply that, with rising temperatures in the future, the extent of desertification will not always continue, desertification will likely reverse at the front and margin of deserts and gobi, and responses of desertification to climate change have significant spatial differences.

STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations

STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations

Next-generation ensemble projections reveal higher climate risks for marine ecosystems

Projections of climate change impacts on marine ecosystems have revealed long-term declines in global marine animal biomass and unevenly distributed impacts on fisheries. Here we apply an enhanced suite of global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), forced by new-generation Earth system model outputs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to provide insights into how projected climate change will affect future ocean ecosystems. Compared with the previous generation CMIP5-forced Fish-MIP ensemble, the new ensemble ecosystem simulations show a greater decline in mean global ocean animal biomass under both strong-mitigation and high-emissions scenarios due to elevated warming, despite greater uncertainty in net primary production in the high-emissions scenario. Regional shifts in the direction of biomass changes highlight the continued and urgent need to reduce uncertainty in the projected responses of marine ecosystems to climate change to help support adaptation planning.

Integrated modeling of human-earth system interactions: An application of GCAM-fusion

Typically in human-earth system modeling studies, feedbacks between the earth and human systems are analyzed by passing information between independent models. The reliance on existing Earth System Model outputs limits the ability to explore feedbacks under arbitrary scenarios and equally important limits the ability to explore large-scale uncertainty in these interactions. In this study we explore a wide range of climate uncertainties and incorporate the implications of increased cooling hydrofluorocarbons emissions. We implement a statistical relationship between global mean temperature change and heating and cooling degree days that allows us to produce changes in building energy demands within GCAM at every time step and every region. While there is a general agreement in the literature that increasing temperatures will increase cooling energy demands and decrease heating energy demands, there has been no fully-coupled analysis of this dynamic that would, for example, account for the feedbacks on hydrofluorocarbons from increased cooling demands. The variation in the spatial distribution of temperatures leads to substantial variation in the change in cooling and heating energy across regions, with regions like USA, India and Sub-Saharan Africa experiencing a factor of two difference in cooling demands. While the feedbacks between building energy demand and global mean temperature are modest by themselves, this study prompts future research on coupled human-earth system feedbacks, in particular in regards to land, water, and other energy infrastructure.

Vatnajökull Mass Loss Under Solar Geoengineering Due to the North Atlantic Meridional Overturning Circulation

AbstractThe objective of solar geoengineering by stratospheric aerosol injection (SAI) is to lower global temperatures, but it may also have adverse side effects. Iceland is situated close to the overturning regions of the Atlantic Meridional Overturning Circulation (AMOC) that warms the North Atlantic area. Hence, this may be one region where reduced irradiance by SAI may not be successful in reducing impacts from greenhouse gas warming. We examine this proposition by estimating how the Icelandic Vatnajökull ice cap (VIC) surface mass balance (SMB) and surface runoff changes in response to greenhouse gas and solar geoengineering scenarios over the period 1982–2089. We use the surface energy and mass balance model SEMIC driven by Earth System Model output under the GeoMIP G4, and CMIP RCP4.5 and RCP8.5 greenhouse gas scenarios. Geoengineering significantly reduces VIC near‐surface air temperature by 0.4°C, downward longwave radiation by 2.4 Wm−2 and increases snowfall by 4.9 mm yr−1 relative to RCP4.5. During the SAI period 2020–2069, modeled annual mean SMB under G4, RCP4.5 and RCP8.5 are −0.34 ± 0.18 m yr−1, −0.56 ± 0.06 m yr−1 and −0.66 ± 0.04 m yr−1, respectively; surface runoff reduction under G4 is 6 ± 6% and 7 ± 6% (95% confidence interval uncertainties) compared with that under RCP4.5 and RCP8.5, which is far smaller than the 20 ± 2% and 32 ± 2% reductions for the Greenland ice sheet. The differences may be attributed to the reinvigoration of AMOC under G4 relative to RCP4.5 which brings more heat to Iceland than Greenland, leading to around half the cooling and longwave radiation reductions than for Greenland.

Can machine learning extract the mechanisms controlling phytoplankton growth from large-scale observations? – A proof-of-concept study

Abstract. A key challenge for biological oceanography is relating the physiological mechanisms controlling phytoplankton growth to the spatial distribution of those phytoplankton. Physiological mechanisms are often isolated by varying one driver of growth, such as nutrient or light, in a controlled laboratory setting producing what we call “intrinsic relationships”. We contrast these with the “apparent relationships” which emerge in the environment in climatological data. Although previous studies have found machine learning (ML) can find apparent relationships, there has yet to be a systematic study examining when and why these apparent relationships diverge from the underlying intrinsic relationships found in the lab and how and why this may depend on the method applied. Here we conduct a proof-of-concept study with three scenarios in which biomass is by construction a function of time-averaged phytoplankton growth rate. In the first scenario, the inputs and outputs of the intrinsic and apparent relationships vary over the same monthly timescales. In the second, the intrinsic relationships relate averages of drivers that vary on hourly timescales to biomass, but the apparent relationships are sought between monthly averages of these inputs and monthly-averaged output. In the third scenario we apply ML to the output of an actual Earth system model (ESM). Our results demonstrated that when intrinsic and apparent relationships operate on the same spatial and temporal timescale, neural network ensembles (NNEs) were able to extract the intrinsic relationships when only provided information about the apparent relationships, while colimitation and its inability to extrapolate resulted in random forests (RFs) diverging from the true response. When intrinsic and apparent relationships operated on different timescales (as little separation as hourly versus daily), NNEs fed with apparent relationships in time-averaged data produced responses with the right shape but underestimated the biomass. This was because when the intrinsic relationship was nonlinear, the response to a time-averaged input differed systematically from the time-averaged response. Although the limitations found by NNEs were overestimated, they were able to produce more realistic shapes of the actual relationships compared to multiple linear regression. Additionally, NNEs were able to model the interactions between predictors and their effects on biomass, allowing for a qualitative assessment of the colimitation patterns and the nutrient causing the most limitation. Future research may be able to use this type of analysis for observational datasets and other ESMs to identify apparent relationships between biogeochemical variables (rather than spatiotemporal distributions only) and identify interactions and colimitations without having to perform (or at least performing fewer) growth experiments in a lab. From our study, it appears that ML can extract useful information from ESM output and could likely do so for observational datasets as well.