General Circulation Model Climate Research Articles

Risk transference between climate variability and financial derivatives: Implications for global food security

We investigate the impacts of the El Niño-Southern Oscillation (ENSO) on global food security by using information embedded in financial derivatives. Using a state-of-the-art general circulation model (GCM) climate forecasting system that generates observations on the phase and magnitude of ENSO, we create novel indices that track its uncertainty throughout time. Together with the option implied volatilities of core food inputs (wheat, maize, rice, and soybean), we model the time-varying volatility spillover from ENSO to each commodity, capturing the direction and intensity of risk transference. Simulating Gaussian and non-Gaussian impulse responses that mimic an increase in climate variability show a persistent impact on the price uncertainty of the commodities lasting up to three months. We also show that shocks are contingent on the phase of ENSO, with warmer conditions in the Eastern Pacific (i.e., the El Niño phase) increasing risk transference for soybean and rice. In contrast, the La Niña phase has a material influence on the uncertainty of wheat and maize. In all, we reveal a volatility transmission channel of climate variability that affects financial markets, suggesting valuable inferences about the impact of climate shocks can be derived from the rich information embedded in commodity-linked derivatives.

Disk‐Integrated Earth’s Outgoing Longwave Radiation Viewed From a Moon‐Based Platform: Model Simulations

AbstractA Moon‐based sensor can observe the Earth as a single point and achieve disk‐integrated measurements of outgoing longwave radiation (OLR), which significantly differs from low orbital, geostationary, and Sun–Earth L1 point platforms. In this study, a scheme of determining the disk‐integrated Earth’s OLR based on a Moon‐based platform is proposed. The observational solid angle was theoretically derived based on the Earth’s ellipsoid model and the disk‐integrated observational anisotropic factor was estimated to eliminate the effects of the Earth’s radiant anisotropy. The simulated disk‐integrated Earth's OLR obtained from a Moon‐based platform varies periodically, due to changes in the observation geometry and Earth's scene distribution within the observed Earth’s disk. Clouds, meteorological parameters, and the land cover distribution notably affect the disk‐integrated Earth’s OLR. By analyzing the disk‐integrated Earth’s OLR from a Moon‐based platform, significant variabilities were investigated. Additionally, the Earth’s shape and radiant anisotropy that affecting the disk‐integrated Earth’s OLR were estimated. In conclusion, a more realistic Earth’s shape, the latest version of the angular distribution model (ADM), and accurate land cover and meteorological datasets are needed when determining the disk‐integrated Earth’s OLR. It is expected the unique variability captured by this platform and its ability to complement traditional satellite data make it a valuable tool for studying Earth’s radiation budget and energy cycle, and contributing to diagnostic of the climate General Circulation Models (GCM) performance.

Impact of climate change on the heating and cooling load components of an archetypical residential room in major Indian cities

Residential heating and cooling (H/C) accounts for ∼7 % of India's electricity consumption. A warming climate will increase residential cooling requirements while heating needs will decrease — an alarming consequence for India, which has predominantly cooling requirements. Thus, to reduce the Indian building sector's energy and carbon footprint, it is essential to assess the impact of climate change on future H/C needs and develop energy-efficiency solutions.This research evaluated the effect of climate change on the H/C energy needs of an archetypical residential room in India by conducting energy simulations for current and future climates. We developed future weather files for eight major Indian cities, covering all climate zones of the country, using predictions from multiple general circulation climate models under two different representative concentration pathways (RCPs): RCP4.5 and RCP8.5. We also developed a novel approach to quantify the H/C load components (walls, windows, infiltration, etc.) for identifying building elements to target for improving energy efficiency.The ensemble median of climate model projections showed that the room's cooling energy demand would increase by 20–179 % by the 2090s compared to the 1990s, depending on the room's orientation, city, and emission scenario. Walls and windows account for over 63 % of the current and future cooling needs and should be the prime targets for energy-efficiency measures. In contrast to rising cooling needs, the room's heating energy demand will decrease by 34–100 % by the 2090s. External walls contribute to over 67 % of the heating needs; thus, insulating them could effectively reduce the heating demand.

On the Arctic Amplification of surface warming in a conceptual climate model

Over the last century Earth’s surface temperatures have warmed by order 1 K as a global average, but with significant variation in latitude: there has been most surface warming at high Northern latitudes, around 3 times more than in low latitude regions (termed Arctic Amplification), while there has been least warming over the Southern Ocean. Many contributing processes have been suggested to explain this asymmetrical latitudinal warming pattern, but quantification of the contributing factors responsible remains elusive. Complex general circulation climate models can reproduce similar asymmetrical patterns of warming, but it can be difficult to interpret the contributing processes. Meanwhile, idealised conceptual energy balance climate models have been able to reproduce a general polar amplification of warming whose origins can be interpreted, but this warming is often symmetrical across both hemispheres and may not be responsible for the real-world pattern. Here, we use a conceptual Energy Balance Model, with imposed closures for initial horizontal diffusivity and cloudiness drawing upon observational constraints and including temperature-dependent diffusivity and a sub-surface ocean heat reservoir, to show that the magnitude of present-day Arctic Amplification may arise through relatively simple thermodynamic (Clausius–Clapeyron) and radiative (climate feedback) processes. The current asymmetry between hemispheric warming may arise due to the transient heat transport up through the base of the surface ocean mixed layer from the slow-responding deep ocean to the fast-responding surface ocean being dominated by upwelling in the Southern Ocean. It should be noted that the processes identified here are not a unique in offering a potential solution, and so significant, or dominant, roles for dynamical processes remain plausible explanations for Arctic Amplification.

Concurrent daytime and nighttime heatwaves in the late 21st century over the CORDEX‐East Asia phase 2 domain using multi‐GCM and multi‐RCM chains

AbstractThe adverse impacts of extreme heat on human health when a concurrent daytime and nighttime heatwave (CDNHW) occurs are greater than when daytime or nighttime heatwaves occur individually, because of the reduced recovery time from heat exposure. This study projects increases in CDNHW over the whole of East Asia under two Representative Concentration Pathway scenarios (RCP2.6 and RCP8.5) and two Shared Socioeconomic Pathway scenarios (SSP1‐2.6 and SSP5‐8.5). The daily maximum and minimum temperatures, which are used to define a CDNHW, are calculated of 3‐hourly temperatures of 25 km horizontal resolution produced by 12 general circulation model and regional climate model chains participating in the Coordinated Regional Climate Downscaling Experiment East Asia phase 2 project. In Historical simulation (1981–2005), occurrence period and occurrence rate of CDNHW from April to September area‐averaged in East Asia are 10.9 days and 0.9%, respectively. In projections for the future (2071–2100), occurrence period and occurrence rate of CDNHW will be 3 weeks and 3.7% (RCP2.6), 2 months and 20.5% (RCP8.5), 2 months and 15.6% (SSP1‐2.6), and 3 months and 45.7% (SSP5‐8.5). In addition, it is expected that the CDNHW intensity will increase, and the spatial extent of CDNHW will be extended. Although a CDNHW lasting less than 3 days is the most common, the proportion of CDNHWs lasting more than 10 days, compared to the total CDNHW frequency, will increase to 1.2% (RCP2.6), 7.2% (RCP8.5), 6.1% (SSP1‐2.6), and 17.3% (SSP5‐8.5) from 0.2% (Historical). Both occurrence rate and intensity of CDNHW will increase to a relatively large extent in Indochina, East and West China, and India. If the current greenhouse gas emissions continue, East Asia will experience unprecedented heat stress because the frequency and intensity of CDNHWs, which rarely occur during present‐day, will increase significantly over all regions by the end of the 21st century.

Effects of Climate Change on Navigability Indicators of the Lower Athabasca River, Canada

The lower Athabasca River (Canada) has experienced notable declines in streamflow and increasing oil sands development since the 1970s. This study investigates the potential impacts of climate change on navigability using both observed historical and projected future flows derived via hydrological simulations driven by an ensemble of statistically downscaled general circulation model climate data. Our use of proposed indices that form the Aboriginal Navigation Index (ANI) and a new index based on percentage over threshold (POT) occurrences yielded novel insights into anticipated changes to the flow regime. Comparisons of near (2041–2070) and far (2071–2100) future periods with the historical baseline (1981–2010) yielded results that project significant reductions in the 500 m3 s−1 POT during the fall navigability period spanning weeks 34 to 43, as well as reductions in the integrated ANIFall. These results indicate that challenging navigational conditions may become more frequent in the second half of the 21st century, not only during this fall period but also earlier into the summer, due to a shift in the flow regime, with potentially severe impacts on the users of the river channels. Our assessment approach is transferable to other regional study areas and should be considered in water management and environmental flow frameworks.

Large Ensemble Particle Filter for Spatial Climate Reconstructions Using a Linear Inverse Model

AbstractProxy records that document the last 2000 years of climate provide evidence for the wide range of the natural climate variability from inter‐annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) Particle filter (PF) that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP‐class GCMs. We examine in a perfect‐model framework the skill of the various LIMs to forecast the dynamics of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR PF. Our results show that the LIMs allow for skillful ensemble forecasts at 1‐year lead‐time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM‐emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR PF.

Modeling general circulation model bias via a combination of localized regression and quantile mapping methods

Abstract. General circulation model (GCM) outputs are a primary source of information for climate change impact assessments. However, raw GCM data rarely are used directly for regional-scale impact assessments as they frequently contain systematic error or bias. In this article, we propose a novel extension to standard quantile mapping that allows for a continuous seasonal change in bias magnitude using localized regression. Our primary goal is to examine the efficacy of this tool in the context of larger statistical downscaling efforts on the tropical island of Puerto Rico, where localized downscaling can be particularly challenging. Along the way, we utilize a multivariate infilling algorithm to estimate missing data within an incomplete climate data network spanning Puerto Rico. Next, we apply a combination of multivariate downscaling methods to generate in situ climate projections at 23 locations across Puerto Rico from three general circulation models in two carbon emission scenarios: RCP4.5 and RCP8.5. Finally, our bias-correction methods are applied to these downscaled GCM climate projections. These bias-correction methods allow GCM bias to vary as a function of a user-defined season (here, Julian day). Bias is estimated using a continuous curve rather than a moving window or monthly breaks. Results from the selected ensemble agree that Puerto Rico will continue to warm through the coming century. Under the RCP4.5 forcing scenario, our methods indicate that the dry season will have increased rainfall, while the early and late rainfall seasons will likely have a decline in total rainfall. Our methods applied to the RCP8.5 forcing scenario favor a wetter climate for Puerto Rico, driven by an increase in the frequency of high-magnitude rainfall events during Puerto Rico's early rainfall season (April to July) as well as its late rainfall season (August to November).

ICON‐O: The Ocean Component of the ICON Earth System Model—Global Simulation Characteristics and Local Telescoping Capability

AbstractWe describe the ocean general circulation model Icosahedral Nonhydrostatic Weather and Climate Model (ICON‐O) of the Max Planck Institute for Meteorology, which forms the ocean‐sea ice component of the Earth system model ICON‐ESM. ICON‐O relies on innovative structure‐preserving finite volume numerics. We demonstrate the fundamental ability of ICON‐O to simulate key features of global ocean dynamics at both uniform and non‐uniform resolution. Two experiments are analyzed and compared with observations, one with a nearly uniform and eddy‐rich resolution of ∼10 km and another with a telescoping configuration whose resolution varies smoothly from globally ∼80 to ∼10 km in a focal region in the North Atlantic. Our results show first, that ICON‐O on the nearly uniform grid simulates an ocean circulation that compares well with observations and second, that ICON‐O in its telescope configuration is capable of reproducing the dynamics in the focal region over decadal time scales at a fraction of the computational cost of the uniform‐grid simulation. The telescopic technique offers an alternative to the established regionalization approaches. It can be used either to resolve local circulation more accurately or to represent local scales that cannot be simulated globally while remaining within a global modeling framework.

A non-stationary extreme-value approach for climate projection ensembles: application to snow loads in the French Alps

Abstract. Anticipating risks related to climate extremes often relies on the quantification of large return levels (values exceeded with small probability) from climate projection ensembles. Current approaches based on multi-model ensembles (MMEs) usually estimate return levels separately for each climate simulation of the MME. In contrast, using MME obtained with different combinations of general circulation model (GCM) and regional climate model (RCM), our approach estimates return levels together from the past observations and all GCM–RCM pairs, considering both historical and future periods. The proposed methodology seeks to provide estimates of projected return levels accounting for the variability of individual GCM–RCM trajectories, with a robust quantification of uncertainties. To this aim, we introduce a flexible non-stationary generalized extreme value (GEV) distribution that includes (i) piecewise linear functions to model the changes in the three GEV parameters and (ii) adjustment coefficients for the location and scale parameters to adjust the GEV distributions of the GCM–RCM pairs with respect to the GEV distribution of the past observations. Our application focuses on snow load at 1500 m elevation for the 23 massifs of the French Alps. Annual maxima are available for 20 adjusted GCM–RCM pairs from the EURO-CORDEX experiment under the scenario Representative Concentration Pathway (RCP) 8.5. Our results show with a model-as-truth experiment that at least two linear pieces should be considered for the piecewise linear functions. We also show, with a split-sample experiment, that eight massifs should consider adjustment coefficients. These two experiments help us select the GEV parameterizations for each massif. Finally, using these selected parameterizations, we find that the 50-year return level of snow load is projected to decrease in all massifs by −2.9 kN m−2 (−50 %) on average between 1986–2005 and 2080–2099 at 1500 m elevation and RCP8.5. This paper extends the recent idea to constrain climate projection ensembles using past observations to climate extremes.

Forecasting commodity returns by exploiting climate model forecasts of the El Niño Southern Oscillation

Abstract The physical and socioeconomic environments in which we live are intrinsically linked over a wide range of time and space scales. On monthly intervals, the price of many commodities produced predominantly in tropical regions covary with the dominant mode of climate variability in this region, namely the El Niño Southern Oscillation (ENSO). Here, for the spot prices returns of vegetable oils produced in Asia, we develop autoregressive (AR) models with exogenous ENSO indices, where for the first time these indices are generated by a purpose-built state-of-the-art general circulation model (GCM) climate forecasting system. The GCM is a numerical simulation which couples together the atmosphere, ocean, and sea ice, with the initial conditions tailored to maximize the climate forecast skill at multiyear timescales in the tropics. To serve as additional benchmarks, we also test commodity forecasts using: (a) no ENSO information as a lower bound; (b) perfect future ENSO knowledge as a reference upper bound; and (c) an econometric AR model of ENSO. All models adopting ENSO factors outperform those that do not, indicating the value here of incorporating climate knowledge into investment decision-making. Commodity forecasts adopting perfect ENSO factors have statistically significant skill out to 2 years. When adopting the GCM-ENSO factors, there is predictive power of the commodity beyond 1 year in the best case, which consistently outperforms commodity forecasts adopting an AR econometric model of ENSO.

Sensitivity of Arctic sea ice to melt pond processes and atmospheric forcing: A model study

Melt ponds are pools of meltwater forming principally on Arctic sea ice during the melt season. The albedo of melt ponds is a key component of the surface energy balance. For this reason, various melt pond schemes have been developed for climate models. These schemes require assumptions on the physical processes governing melt ponds as well as a knowledge of the atmospheric state, which are not perfectly known. In this study, we investigate the effects of the sources of uncertainty from the prescribed atmospheric surface state, the melt pond scheme definition and the refreezing formulation of melt ponds on the simulated Arctic sea ice and melt pond properties with the NEMO-LIM3 ocean–sea ice general circulation model. We find that the simulated melt pond state is largely controlled by the freezing point of melt ponds. The representation of melt ponds is in better agreement with observations when using the freezing point of −0.15°C compared to the value of −2.00°C, in our model set-up. All the simulations feature positive trends in melt pond area fraction over the past decades. However, only 3 out of 8 simulations have significant positive trends in melt pond volume per sea ice area. This suggests an influence of the sea ice state for melt ponds over the last 30 years. Overall, we find that the simulated sea ice state, and in particular sea ice volume, is more affected by changes in the prescribed atmospheric forcing than by changes in the prescribed melt pond scheme or refreezing formulation. Including explicit melt pond schemes in large-scale sea-ice models offer the possibility to improve the representation of the surface energy balance in climate general circulation models. Our results underline that, in parallel to these efforts in model developments, improved estimates of surface atmospheric conditions will be required to achieve more realistic sea ice states.

Sensitivity of Climate Feedbacks to Vertical Resolution in a General Circulation Model

AbstractMany of the current, Coupled Model Intercomparison Project 6 (CMIP6), General Circulation Models (GCMs) show climate sensitivity higher than currently accepted uncertainty ranges. There is a weak correlation between increases in vertical resolution and in climate sensitivity from CMIP5. In particular, the MO Hadley Centre GCM’s vertical resolution has more than doubled, and its climate sensitivity has also increased substantially. We therefore compare estimates of climate sensitivity from the CMIP6 model HadGEM3‐GC3.1‐LL, with 85 levels, and a version with 242 levels. This is far higher resolution than in any previously published simulations with a mainstream GCM, though still less than many scales important for clouds. The climate sensitivity and feedbacks, including the cloud feedback, change little. This suggests that vertical resolution did not drive recent increases in GCM climate sensitivity, though this result should be further tested in other GCMs and over a broader range of resolutions.

Factors Governing Cloud Growth and Entrainment Rates in Shallow Cumulus and Cumulus Congestus During GoAmazon2014/5

AbstractShallow cumulus and cumulus congestus clouds play an important role in the large‐scale tropical circulation by mixing heat and moisture vertically and preconditioning the environment for deeper convection. Different representations of these shallow clouds account for much of the spread in General Circulation Model (GCMs) climate sensitivity, potentially because of how entrainment is represented in GCM parameterizations. This study uses observations from the Department of Energy's Atmospheric Radiation Measurement (ARM) mobile facility deployed at Manacapuru, Brazil, during the Green Ocean Amazon (GoAmazon2014/5) Campaign. Environmental thermodynamic profiles and observations of cloud top height (CTH) are used to constrain an entraining plume model to estimate bulk entrainment rates (ERs). Estimates of CTH are obtained from a combination of vertically pointing W‐band ARM cloud radar and 1,290 MHz Radar Wind Profiler observations. A combination of radiosonde, microwave radiometer profiler, and microwave radiometer observations provides new best estimates of the environmental thermodynamic state. We quantify uncertainty in ERs considering uncertainties in estimated CTH, environmental thermodynamic properties, and assumed initial parcel characteristics. We find ERs ranging from 0.16 to 2.8 km−1 with an average of 0.58 ± 0.10 km−1 over a selected population of 469 shallow cumulus and cumulus congestus clouds. Using the retrieved estimates of ER, we evaluate several entrainment closures that are currently used in atmospheric models or have been proposed based on theory or large eddy simulation. Entrainment rates in cumulus clouds are weakly correlated with low‐level buoyancy, cloud depth, and cloud size.

Impact of Stochastic Physics and Model Resolution on the Simulation of Tropical Cyclones in Climate GCMs

AbstractThe role of model resolution in simulating geophysical vortices with the characteristics of realistic tropical cyclones (TCs) is well established. The push for increasing resolution continues, with general circulation models (GCMs) starting to use sub-10-km grid spacing. In the same context it has been suggested that the use of stochastic physics (SP) may act as a surrogate for high resolution, providing some of the benefits at a fraction of the cost. Either technique can reduce model uncertainty, and enhance reliability, by providing a more dynamic environment for initial synoptic disturbances to be spawned and to grow into TCs. We present results from a systematic comparison of the role of model resolution and SP in the simulation of TCs, using EC-Earth simulations from project Climate-SPHINX, in large ensemble mode, spanning five different resolutions. All tropical cyclonic systems, including TCs, were tracked explicitly. As in previous studies, the number of simulated TCs increases with the use of higher resolution, but SP further enhances TC frequencies by ~30%, in a strikingly similar way. The use of SP is beneficial for removing systematic climate biases, albeit not consistently so for interannual variability; conversely, the use of SP improves the simulation of the seasonal cycle of TC frequency. An investigation of the mechanisms behind this response indicates that SP generates both higher TC (and TC seed) genesis rates, and more suitable environmental conditions, enabling a more efficient transition of TC seeds into TCs. These results were confirmed by the use of equivalent simulations with the HadGEM3-GC31 GCM.

To bias correct or not to bias correct? An agricultural impact modelers’ perspective on regional climate model data

Many open questions and unresolved issues surround the topic of bias correction (BC) in climate change impact studies (CCIS). One question relates to the contribution of downscaling of climate change scenarios on the uncertainties in results obtained using impact models for agriculture. In particular, for large area or regional agricultural impact assessments, the question of bias correction is of high relevance. Relatively few studies exist looking at the quantification of the impacts of BC methods in general circulation model (GCM) and regional climate model (RCM) data on results of such impact studies. Here, we quantify the impact of different BC methods on temperature (T) and precipitation (P) from different CORDEX GCM-RCM combinations, and how the debiased T&P signal may propagate through agricultural impact models. Specifically, we estimate the impact of BC on (i) an empirical P- and fuzzy rule-based algorithm to estimate the crop planting date, and (ii) a mechanistic T&P-based approach to quantify the crop suitability index (CSI) for the main staple crops in West Africa (i.e. groundnut, maize, pearl millet, sorghum). Both approaches serve as a proxy for more complex process-based ecophysiological crop models. Depending on the choice of the BC method, the uncertainties in the assessment of the CSI can be more than twice as high compared to the uncertainties from the GCM-RCM model selection. Comparing the estimated CSI values with observed harvest area, it is found that BC generally improves the performance for models with low hit rates (<30–35%), but decreases the performance for models with relatively high hit rates (>35%). Such consequences can also be expected for process-based crop models, which are developed to operate on field-scale but are driven by coarser scale RCMs. It is concluded that such agriculturally oriented climate impact models as investigated here should be interpreted with great caution if applied without BC or relying on a single BC approach only. Rather, we suggest to include different BC approaches in the generation of climate projections for CCIS and quantify their uncertainties following a super-ensemble probabilistic assessment.

Model-Based Estimation of Agronomic Value Responses to Seasonal Forecasts

Projecting crop yields and aggregate production of the region is of the highest interest for maize production markets which is the source of economic development of the farmers. We used Agriculture Production Systems sIMulation (APSIM 7.10) to simulate the maize yield of Nkotsi region of Rwanda for two seasons SOND and MAM. For this simulation, daily meteorological data of the rainfall, Solar radiation, maximum and minimum temperature are inputs of the model. The impact of the ElNi~no-Southern Oscillation(ENSO)phases and the yield is demonstrated, it is found that ElNi~no phase is good for maize and LaNina lead to the small yield. We compared the simulated yield with observed the author realize that they are correlated with R Square of 0.89. We identified cropping practices of profit-maximizing based on climate information. The seasonal rainfall forecasting has been done by ClimatePredictability Tools(CPT15) and WeatherMan Version 4.7, utilizing General Circulation Model (GCM) and Enhancing National Climate Services (ENACTS) data from 1985 to 2019.

CAFE60v1: A 60-year large ensemble climate reanalysis. Part I: System design, model configuration and data assimilation.

AbstractWe detail the system design, model configuration and data assimilation evaluation for the CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1. CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multi-year climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to present. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in-situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are sub-sampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via explicit cross domain covariances between ocean, atmosphere, sea ice and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean and sea ice. The system produces 96 climate trajectories (state estimates) over the most recent six decades as well as a complete data archive of initial conditions potentially enabling individual forecasts for all members each month over the 60 year period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses.

A participatory approach to assessing groundwater recharge under future climate and land-cover scenarios, Tutuila, American Samoa

Study regionOceania, South Pacific, Polynesia. Study FocusChanging climates have the potential to significantly impact global water resources availability. On many volcanic islands, groundwater is the primary drinking water source, thereby making it essential to manage this limited resource carefully. In this study, we developed high temporal and spatial resolution groundwater recharge estimates for the Island of Tutuila, American Samoa using the Soil Water-Balance-2 (SWB2) model. Additionally, we predicted future recharge by running the calibrated model with combinations of dynamically downscaled general circulation climate model (GCM) predictions, and future land-cover scenarios developed collectively with local stakeholder groups. New hydrological insightsPresent-day results indicate 57 % of Tutuila’s rainfall becomes groundwater recharge, 8 % evaporates from the canopy, 15 % evapotranspires, and 20 % discharges as stormflow-runoff. Future climate scenarios suggest recharge may increase by 8 % or 14 % depending on global emissions. Land-cover was a less significant driver of hydrologic change, although increases in impervious surfaces showed a negative impact on recharge. This work is maintained as an active open-source project on GitHub, the world’s leading software development platform, thereby enhancing transparency, reproducibility, and participation from stakeholders and managers in American Samoa. This study is the first of its kind from a location within the South Pacific Convergence Zone, and provides insights into how human activities on global and local levels affect the future sustainability of essential resources.

Ensemble Kalman Filter Parameter Estimation of Ocean Optical Properties for Reduced Biases in a Coupled General Circulation Model

AbstractCoupled general circulation models (GCM), and their atmospheric, oceanic, land, and sea‐ice components have many parameters. Some parameters determine the numerics of the dynamical core, while others are based on our current understanding of the physical processes being simulated. Many of these parameters are poorly known, often globally defined, and are subject to pragmatic choices arising from a complex interplay between grid resolution and inherent model biases. To address this problem, we use an ensemble transform Kalman filter, to estimate spatiotemporally varying maps of ocean albedo and shortwave radiation e‐folding length scale in a coupled climate GCM. These parameters are designed to minimize the error between short term (3–28 days) forecasts of the climate model and a network of real world atmospheric, oceanic, and sea‐ice observations. The data assimilation system has an improved fit to observations when estimating ocean albedo and shortwave e‐folding length scale either individually or simultaneously. However, only individually estimated maps of shortwave e‐folding length scale are also shown to systematically reduce bias in longer multiyear climate forecasts during an out‐of‐sample period. The bias of the multiyear forecasts is reduced for parameter maps determined from longer DA cycle lengths.