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Monthly mean North Atlantic dynamic topography from the AVISO satellite analysis versus the CM4.0 simulation. We chose months with comparable realizations of the Gulf of Mexico loop current.
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We describe the Geophysical Fluid Dynamics Laboratory's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model form...
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Antarctic sea ice has paradoxically become more extensive over the past four decades despite a warming climate. The regional expression of this trend has been linked to changes in vertical redistribution of ocean heat and large-scale wind-field shifts. However, the short length of modern observations has hindered attempts to attribute this trend to...
One of the major globally relevant systematic biases in previous generations of climate models has been an equatorward bias in the latitude of the Southern Hemisphere (SH) mid‐latitude tropospheric eddy driven westerly jet. The far reaching implications of this for Southern Ocean heat and carbon uptake and Antarctic land and sea ice are key reasons...
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... Here, we use two additional high resolution AM4 versions with 192 × 192 (c192) and 384 × 384 (c384) grid boxes per cube face, corresponding to horizontal resolutions of ∼50 km and ∼25 km, respectively. The default GFDL AM4.0 (Zhao et al., 2018a(Zhao et al., , 2018b serves as the atmospheric component of GFDL's physical climate model (CM4, Held et al., 2019), which participated in Phase 6 of the Coupled Model Intercomparison Project (CMIP6, Eyring et al., 2016). c192AM4 (Zhao, 2020) participated in the CMIP6 High Resolution Model Intercomparison Project (HighResMIP, Haarsma et al., 2016). ...
We examine tropical rainfall from the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4 (GFDL AM4) at three horizontal resolutions of 100 km, 50 km, and 25 km. The model produces more intense rainfall at finer resolutions, but a large discrepancy still exists between the simulated and the observed frequency distribution. We use a theoretical precipitation scaling diagnostic to examine the frequency distribution of the simulated rainfall. The scaling accurately produces the frequency distribution at moderate‐to‐high intensity (≥10 mm day⁻¹). Intense tropical rainfall at finer resolutions is produced primarily from the increased contribution of resolved precipitation and enhanced updrafts. The model becomes more sensitive to the grid‐scale updrafts than local thermodynamics at high rain rates as the contribution from the resolved precipitation increases.
... Our dataset covered contemporary conditions spanning the years 1981-2000 and included multiple future datasets representing shared socioeconomic pathways: ssp126 'sustainability', ssp370 'regional rivalry', and ssp585 'fossil-fueled development'. These datasets were derived from various climate models, namely GFDL_ESM4 (Held et al., 2019), IPSL_CM6A_LR (Boucher et al., 2020), MPI_ESM1_2_HR (Gutjahr et al., 2019), MRI_ESM2_0 (Yukimoto et al., 2019), and UKESM1_0_LL (Sellar et al., 2019). ...
Climate change and human influence are transforming mountain ecosystems, significantly impacting species distributions and biodiversity. Among these changes, the upward migration of lowland species into mountain regions stands out. This study examines the ecogeographical niche overlap and genetic diversity among three Leucanthemum species in the Carpathian Mountains distributed along an altitudinal gradient: the lowland L. ircutianum (4 x ), the montane L. rotundifolium (2 x ), and the alpine L. gaudinii (2 x ). By genotyping over 600 individuals using SNP analysis, followed by Principal Coordinate Analysis (PCoA), Neighbor-Net Network, and Structure clustering, we reveal not just distinct genetic groups but also hybridization across all species, suggesting the potential for triple hybrids. Genetic admixture is further supported by environmental background and niche overlap analyses that reveal substantial overlap among species, particularly in line with their vertical distribution. Climate envelope plots indicate a likely reduction in available habitat for mountainous species due to climate change, leading to an increase in competition and an intensification of hybridization. Anthropogenic influences are further intensifying these hybridization trends. Among the studied species, L. gaudinii is most at risk of overwhelming hybridization, whereas L. ircutianum may experience habitat expansion. By providing a comprehensive genetic and ecological overview, our research highlights the significance of hybridization in biodiversity conservation and the challenges posed by environmental changes and anthropogenic activities in mountain environments. This study not only contributes to the understanding of genetic diversity in the Carpathians but also underscores the broader implications for molecular ecology and conservation strategies in mountain ecosystems.
... SPEAR is composed of GFDL's AM4 atmosphere, LM4 land, MOM6 ocean, and SIS2 sea-ice models. These component models are the same as GFDL's GCM version 4 (CM4) (Held et al., 2019), which is a contributor to the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Lee & Romero, 2023;O'Neill et al., 2016). CM4's modeling improvements over previous generations include decreased surface temperature, precipitation, and shortwave and longwave heat flux biases, as well as improved El Niño-Southern Oscillation (ENSO) representation (Held et al., 2019). ...
... These component models are the same as GFDL's GCM version 4 (CM4) (Held et al., 2019), which is a contributor to the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Lee & Romero, 2023;O'Neill et al., 2016). CM4's modeling improvements over previous generations include decreased surface temperature, precipitation, and shortwave and longwave heat flux biases, as well as improved El Niño-Southern Oscillation (ENSO) representation (Held et al., 2019). To further optimize SPEAR for seasonal and decadal predictions of extremes, ocean resolution was reduced to reallocate computational costs to producing larger ensembles . ...
Seasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow‐tourism economies. While the frequency and severity of snow droughts (SD), that is, anomalously low snowpacks, are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify with observations alone. Using a 30‐member large ensemble from a state‐of‐the‐art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations‐based data set, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5‐8.5 (SSP5‐8.5) and 5 times more frequent under SSP2‐4.5, compared to a 1921–2011 average. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track late winter and spring snowpack across WUS watersheds, finding differences in the onset time of a “no‐snow” threshold between regions and large internal variability within the ensemble that are both on the order of decades. We attribute the inter‐regional variability to differences in the regions' mean winter temperature and the intra‐regional variability to irreducible internal climate variability which is not well‐explained by temperature variations alone. Despite strong scenario forcing, internal climate variability will continue to drive variations in SD and no‐snow conditions through 2100.
... To narrow down cloud feedback uncertainty and improve climate models' fidelity in future projections, it would be natural to focus on reducing model biases in simulations of the observed present-day CRE. Climate model developments often tune present-day CRE toward observed long-term mean climatology (Danabasoglu et al., 2020;Donner et al., 2011;Golaz et al., 2019;Held et al., 2019;Zhao et al., 2018). However, models with similar climatological CRE can still differ substantially due to compensating errors among various weather regimes. ...
Using high temporal resolution satellite observations and reanalysis data, we classify daily weather into distinct regimes and quantify their associated cloud radiative effect (CRE) to better understand the roles of various weather systems in affecting Earth's top‐of‐atmosphere radiation budget. These regimes include non‐precipitation, drizzle, wet non‐storm, and storm days, which encompass atmospheric rivers (AR), tropical storms (TS), and mesoscale convection systems (MCS). We find that precipitation (wet) days account for roughly 80% (60%) of global longwave (LW) and shortwave (SW) CREs due to their large frequency and high intensity in CRE. Despite being rare globally (13%), AR, TS, and MCS days together account for 32% of global LW CRE and 27% of SW CRE due to their higher intensity in LW and SW CRE. These results enhance our understanding of how various weather systems, particularly severe storms, influence Earth's radiative balance, and will help to better constrain climate models.
... The TOA flux improvements likely contributed to the precipitation improvements by improving the balances of radiative cooling and latent heating. The improvement in the newer model version is consistent with that documented by Held et al. (2019) and evident via the arrow directions pointing to the observational reference point. ...
... Each vertical axis indicates performance for each metric defined for the climatology of variables (i.e., temporally averaged spatial RMSE of annual cycle climatology patterns; Fig. 12a), ENSO characteristics ( Fig. 12b), or inter-annual variability mode obtained from seasonal or monthly averaged time series (Fig. 12c). It is shown that GFDL-CM4 is superior to GFDL-CM3 for most cases across selected metrics (downward arrows in green), while inferior for a few cases (upward arrows in red), which is consistent with previous findings (Held et al., 2019;Planton et al., 2021;Chen et al., 2021). Such applications of the parallel coordinate plot can enable quick overall assessment and tracking of the ESM performance evolution during its development cycle. ...
Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large-to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.
... Few studies have examined what is driving the EEI increase since 2000. Raghuraman et al. (2021) used Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al. 2016) simulations from the Geophysical Fluid Dynamics Laboratory Coupled/ Atmospheric Model 4.0 (GFDL CM4/AM4) (Zhao et al. 2018;Held et al. 2019) to assess the contributions of internal variability, effective radiative forcing (ERF) and climate feedbacks on the CERES trend. They conclude that the positive EEI trend can only be explained if the simulations account for the increase in anthropogenic radiative forcing and associated climate response since 2000. ...
Satellite observations from the Clouds and the Earth’s Radiant Energy System show that Earth’s energy imbalance has doubled from 0.5 ± 0.2 Wm⁻² during the first 10 years of this century to 1.0 ± 0.2 Wm⁻² during the past decade. The increase is the result of a 0.9 ± 0.3 Wm⁻² increase absorbed solar radiation (ASR) that is partially offset by a 0.4 ± 0.25 Wm⁻² increase in outgoing longwave radiation (OLR). Despite marked differences in ASR and OLR trends during the hiatus (2000–2010), transition-to-El Niño (2010–2016) and post-El Niño (2016–2022) periods, trends in net top-of-atmosphere flux (NET) remain within 0.1 Wm⁻² per decade of one another, implying a steady acceleration of climate warming. Northern and southern hemisphere trends in NET are consistent to 0.06 ± 0.31 Wm⁻² per decade due to a compensation between weak ASR and OLR hemispheric trend differences of opposite sign. We find that large decreases in stratocumulus and middle clouds over the sub-tropics and decreases in low and middle clouds at mid-latitudes are the primary reasons for increasing ASR trends in the northern hemisphere (NH). These changes are especially large over the eastern and northern Pacific Ocean, and coincide with large increases in sea-surface temperature (SST). The decrease in cloud fraction and higher SSTs over the NH sub-tropics lead to a significant increase in OLR from cloud-free regions, which partially compensate for the NH ASR increase. Decreases in middle cloud reflection and a weaker reduction in low-cloud reflection account for the increase in ASR in the southern hemisphere, while OLR changes are weak. Changes in cloud cover in response to SST increases imply a feedback to climate change yet a contribution from radiative forcing or internal variability cannot be ruled out.
... The remarkable performance by the few uncorrected CMIP6 model like NorESM2-MM can be attributed to enhancements to the conservation of energy and angular momentum, enhanced deep convection, improved aerosol handling, upgraded parameterization of sea-salt emissions, online emissions of mineral dust, and optimized heterogeneous ice nucleation treatment (Seland et al. 2020). Likewise, the better performance for GFDL-CM4 models could be attributed to a revised approach to the bottom border; a new ocean code; a new sea ice model called SIS2.0; a new surface boundary layer approach; significant updates to the radiation code; a novel convective closure for shallow and deep convection known as 'double-plume' (Held et al. 2019). As for CanESM5, relative to its predecessor, the new model's better performance could be linked to the completely new models for the ocean, sea ice, and marine ecosystems, and a new coupler that is capable to simulate the observed climate much better (Swart et al. 2019). ...
In the era of Anthropocene climate that the world is currently experiencing, accurate climate models that exhibit minimal uncertainties for precise estimation of the sporadic extreme climate anomalies is urgently needed. To address this gap, the present study quantified the added value in the recently released NEX-GDDP-CMIP6 precipitation models as compared to their native CMIP6 models over 9 climatic zones in Africa in order to identify the best performing models with minimal biases. Accordingly, 22 NEX-GDDP-CMIP6 precipitation models and similar number for native CMIP6 precipitation models were evaluated with respect to two observational products (CHIRPS and CPC). With robust statistical techniques employed, the results showed that at annual and seasonal scales, the NEX-GDDP-CMIP6 GCMs and their multi-model ensemble (MME) reproduced a coherent spatial pattern of precipitation to the observed better than the native CMIP6 GCMs. The NEX-GDDP-CMIP6 GCMs and their MME also exhibited a stronger spatial pattern with higher correlation coefficients, lower mean bias and root mean square error recorded, than in the CMIP6 GCMs. The differences and improvements exhibited by the NEX-GDDP-CMIP6 GCMs, highlight the significance of the improved bias correction method and finer spatial resolution of 0.25*0.25 which characterize the newly published NEX-GDDP-CMIP6 GCMs. The Taylor Skill Score and the Interannual Variability Scores were used to rank the NEX-GDDP-CMIP6 GCMs after evaluation and the results confirmed they were better than the native CMIP6 GCMs in simulating daily precipitation over diverse climate zones of Africa. It is recommended that new future projections of precipitation under whatever scenario (SSPs) or region should adopt this better improved dataset.
... SPEAR is the latest coupled GCM from NOAA GFDL . SPEAR uses similar component models as the GFDL Global Climate Model version 4 (CM4) (AM4 atmosphere and LM4 land model, Held et al., 2019;Zhao et al., 2018; MOM6 ocean and SIS2 sea ice models, Adcroft et al., 2019) but with configurations optimized for the study of seasonal to multidecadal variability, predictability, and projection. SPEAR provides three options for horizontal resolution in the atmosphere and land components: 1°(∼100 km), 0.5°(∼50 km), and 0.25°(∼25 km). ...
The Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long‐term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25‐km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic‐scale events, including atmospheric rivers (AR) and tropical cyclone (TC)‐related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25‐km GFDL‐SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC‐related events, especially those undergoing extratropical transitions. In the future, both AR‐related and TC‐related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5‐8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC‐related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC‐related extreme precipitation over the NEUS.
... To explore and showcase the proposed causal framework, we consider a long, stationary integration of the state-ofthe-art coupled climate model GFDL-CM4 [90]. The ocean component of CM4, named MOM6, has a horizontal grid spacing of 0.25 • and 75 vertical layers [91]. ...
We propose a data-driven framework to describe spatiotemporal climate variability in terms of a few entities
and their causal linkages. Given a high-dimensional climate field, the methodology first reduces its dimension-
ality into a set of regionally constrained patterns. Causal relations among such patterns are then inferred in
the interventional sense through the fluctuation-response formalism. To distinguish between true and spurious
responses, we propose an analytical null model for the fluctuation-dissipation relation, therefore allowing us for
uncertainty estimation at a given confidence level. We showcase the methodology on the sea surface temperature
field from a state-of-the-art climate model. The usefulness of the proposed framework for spatiotemporal climate
data is demonstrated in several ways. First, we focus on the correct identification of known causal relations
across tropical basins. Second, we show how the methodology allows us to visualize the cumulative response of
the whole system to climate variability in a few selected regions. Finally, each pattern is ranked in terms of its
causal strength, quantifying its relative ability to influence the system’s dynamics. We argue that the methodology
allows us to explore and characterize causal relations in spatiotemporal fields in a rigorous and interpretable way.
... Two of the models used are Earth System Models, the GFDL Earth System Model version 4.1 (ESM4.1, 0.5°resolution and comprehensive biogeochemistry, Dunne, Horowitz, et al., 2020, Dunne, Bociu, et al., 2020Stock et al., 2020) and GFDL Fourth Generation Climate Model (CM4, 0.25°resolution, simple biogeochemistry, Held et al., 2019). The third model is an ocean circulation and biogeochemistry model forced with atmospheric reanalysis (MOM6-C, 0.125°resolution, comprehensive biogeochemistry. ...
The california current system (CCS) supports a wide array of ecosystem services with hypoxia historically occurring in near‐bottom waters. Limited open ocean data coverage hinders the mechanistic understanding of CCS oxygen variability. By comparing three different models with varying horizontal resolutions, we found that dissolved oxygen (DO) anomalies in the CCS are propagated from shallower coastal areas to the deeper open ocean, where they are advected at a density and velocity consistent with basin‐scale circulation. Since DO decreases have been linked to water mass redistribution in the CCS, we conduct a water mass analysis on two of the models and on biogeochemical Argo floats that sampled multiple seasonal cycles. We found that high variability in biogeochemical variables (DO and nutrients) seen in regions of low variability of temperature and salinity could be linked to water mass mixing, as some of the water masses considered had higher gradients in biogeochemical variables compared to physical variables. Additional DO observations are needed, therefore, to further understand circulation changes in the CCS. We suggest that increased DO sampling north of 35˚N and near the shelf break would benefit model initialization and skill assessment, as well as allow for better assessment of the role of equatorial waters in driving DO in the northern CCS.
