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Final Technical Report for DE‐SC0021983 Combining Long‐Term Observations and Lagrangian Case Studies to Evaluate Stratiform Cloud Precipitation Processes in Climate Models
Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.
Weather and climate extremes are increasingly occurring in the Arctic. Here, in this Review, we evaluate historical and projected changes in rare Arctic extremes across the atmosphere, cryosphere and ocean and elucidate their driving mechanisms. Clear shifts occur in mean and extreme distributions after ~2000. For instance, pre-2000 to post-2000 observational probabilities of 1.5 standard deviation events increase by 20% for atmospheric heat waves, 76.7% for Atlantic layer warm events, 83.5% for Arctic sea ice loss and 62.9% for Greenland Ice Sheet melt extent — in many cases, low probability, rare extreme events in the early period become the norm in the latter period. These observed changes can be explained using a ‘pushing and triggering’ concept, representing interplay between external forcing and internal variability: long-term warming destabilizes the climate system and ‘pushes’ it to a new state, allowing subsequent variability associated with large-scale atmosphere–ocean–ice interactions and synoptic systems to ‘trigger’ extreme events over different timescales. Ongoing anthropogenic warming is expected to further increase the frequency and magnitude of extremes, such that simulated probabilities of 1.5 standard deviation events increase by 72.6% for atmospheric heat waves, 68.7% for Atlantic layer warm events and 93.3% for Greenland Ice Sheet melt rate between historic (1984–2014) and future (2069–2099) periods under a very high emission scenario. Future research should prioritize the development of physically based metrics, enhance high-resolution observation and modelling capabilities and improve understanding of multiscale Arctic climate drivers.
The Arctic Ocean is changing rapidly as the planet warms, but studying it presents challenges: it’s a remote area that is often covered by sea ice. As CHELLE GENTEMANN, MARISOL GARCÍA-REYES and JORGE VAZQUEZ-CUERVO explain, using autonomous solar-powered sailing robots is a safe, reliable and climate-friendly way to collect the data we need.
The atmosphere and ocean are joined together over seventy percent of Earth, with ocean surface vector wind (OSVW) stress one of the linkages. Satellite OSVW measurements provide estimates of wind divergence at the bottom of the atmosphere and wind stress curl at the top of the ocean; both variables are critical for weather and climate applications. As is common with satellite measurements, a multitude of OSVW data products exist for each currently operating satellite instrument. In 2012 the Joint Technical Commission on Oceanography and Marine Meteorology (JCOMM) launched an initiative to coordinate production of OSVW data products to maximize the impact and benefit of existing and future OSVW measurements in atmospheric and oceanic applications. This paper describes meteorological and oceanographic requirements for OSVW data products; provides an inventory of unique data products to illustrate that the challenge is not the production of individual data products, but the generation of harmonized datasets for analysis and synthesis of the ensemble of data products; and outlines a vision for JCOMM, in partnership with other international groups, to assemble an international network to share ideas, data, tools, strategies, and deliverables to improve utilization of satellite OSVW data products for research and operational applications.
Abstract To improve the representation of microphysical processes in convective clouds and their interaction with aerosol and stratiform clouds, a two‐moment convective microphysics parameterization (CMP) scheme developed by Song and Zhang (2011, https://doi.org/10.1029/2010jd014833 ) is upgraded and implemented in E3SM. The new developments include: (a) implementing a parameterization for graupel to enhance the representation of ice‐phase microphysical processes; (b) representing the impact of spatial inhomogeneity of cloud droplets in cumulus ensembles on autoconversion and accretion processes to improve the representation of warm‐rain microphysical processes; (c) implementing a comprehensive Bergeron process parameterization to better represent mixed‐phase microphysical processes; and (d) representing the interactions between ice‐phase microphysics and cloud thermodynamics. Simulations show that the cloud microphysical properties simulated by the CMP are generally in good agreement with observations. It reasonably simulates the changes in droplets effective radius related to precipitation formation in convective clouds, as identified from satellite observations. It also successfully simulates the contrast in these processes between maritime and continental clouds, demonstrating its capability to simulate the impact of aerosols on convection. Analyses of the impact of CMP on climate mean state simulation demonstrate that the CMP slightly improves the simulations of precipitation, cloud macrophysical properties, longwave cloud radiative forcing, zonal wind, and temperature. However, a degradation in shortwave cloud radiative forcing occurs.
The NASA/CNES Jason satellite, follow-on to the highly successful TOPEX/Poseidon mission, continues to provide oceanographers and marine operators across the globe with a continuous twelve-year, high quality stream of sea surface height data. The mission is expected to extend through 2007, when the NASA/NOAA/CNES follow-on mission, OSTM, will be launched with the wide-swath ocean altimeter on board. This unprecedented resource of valuable ocean data is being used to map sea surface height, geostrophic velocity, significant wave height, and wind speed over the global oceans. Altimeter data products are currently used by hundreds of researchers and operational users to monitor ocean circulation and improve our understanding of the role of the oceans in climate and weather. Ocean altimeter data has many societal benefits and has proven invaluable in many practical applications including; a) Ocean forecasting systems; b) Climate research and forecasting; c) Ship routing; d) Fisheries management; e) Marine mammal habitat monitoring; f) Hurricane forecasting and tracking; g) Debris tracking; and h) Precision marine operations such as cable-laying and oil production. The data has been cited in nearly 2,000 research and popular articles since the launch of TOPEX/Poseidon in 1992, and almost 200 scientific users receive the global coverage altimeter data on a monthly basis. In addition to the scientific and operational uses of the data, the educational community has seized the unique concepts highlighted by these altimeter missions as a resource for teaching ocean science to students from grade school through college. This presentation will highlight societal benefits of ocean altimetry data in the areas of climate studies, marine operations, marine research, and non-ocean investigations.
The NASA responsibility and activities for the follow-on to the original Agro-Climatic Environmental Monitoring Project (ACEMP) which was completed during 1987 is described. Five training sessions which comprise the NASA ACEMP follow-on are: Agrometeorology, Meteorology of Severe Storms Using GEMPAK, Satellite Oceanography, Hydrology, and Meteorology with TOVS. The objective of the follow-on is to train Bangladesh Government staff in the use of satellite data for remote sensing applications. This activity also encourages the scientific connection between NASA/Goddard Space Flight Center and The Bangladesh Space and Remote Sensing Organization (SPARRSO).
Since its inception in 1958, NASA has been studying the Earth and its changing environment by observing the atmosphere, oceans, land, ice, and snow, and their influence on weather and climate. We now understand that the key to gaining a better understanding of the global environment is exploring how the Earth's systems of air, land, water, and life interact with each other. This approach-called Earth Systems Science-blends together fields like meteorology, oceanography, geology, and biology. In 1991, NASA launched a more comprehensive program to study the Earth as an integrated environmental system. They call it NASA's Earth Science Enterprise. A major component of the Earth Science Enterprise is the Earth Observing System (EOS). EOS is series of satellites to be launched over the next two decades that will be used to intensively study the Earth, with the hopes of expanding our under- standing of how natural processes affect us, and how we might be affecting them. Such studies will yield improved weather forecasts, tools for managing agriculture and forests, information for fishermen and local planners, and, eventually, the ability to predict how the climate will change in the future. Today's program is laying the foundation for long-term environmental and climate monitoring and prediction. Potentially, this will provide the understanding needed in the future to support difficult decisions regarding the Earth's environment.
The Sequal (Seasonal Response of the Equatorial Atlantic) program has the objective to model correctly the dominant physical processes producing the seasonal redistribution of heat, mass, and salt in the upper ocean. The efforts of Sequal are greatly enhanced by the Focal (Francais Ocean et Climat dans l'Atlantique Equatorial) program. A knowledge of fields of wind stress over the tropical Atlantic is needed for the achievement of the Sequal objectives. The Sequal program makes use of the Fleet Numerical Oceanography Central (FNOC) 'Nogaps' wind fields as a first-guess field which will subsequently be modified. Nogaps is a global weather prediction model consisting of independent global wind and mass analysis submodels. The present investigation is concerned with some preliminary comparisons between Nogaps predictions and surface observations, taking into account monthly mean Nogaps fields and monthly means from a century-long ship report-based climatology.
Equatorial Atlantic surface waters respond directly to changes in zonal and meridional lower tropospheric winds forced by annual insolation. This mechanism has its maximum effect along the equatorial wave guide centered on 10 deg W. The result is to amplify even subtle tropical climate changes such that they are recorded by marked amplitude changes in the proxy signals. Model realizations, NCAR AGCM and OGCM for 0 Ka and 126 Ka (January and July), and paleoceanographic proxy data show that these winds are also forced by insolation changes at the orbital periods of precession and obliquity. Perhelion in boreal summer produces a strengthened monsoon, e.g., increase meridional and decrease zonal wind stress. This reduces oceanic Ekman divergence and thermocline/nutricline shallowing. The result, in the equatorial Atlantic, is reduced primary productivity and higher euphotic zone temperatures; vice versa for perihelion in boreal winter. Perihelion is controlled by precession. Thus, the dominant period in spectra from a stacked SST record (0-252 Ka BP) at the site of the equatorial Atlantic amplifier is 23 Ky (53 percent of the total variance). This precessional period is coherent (k = 0.920) and in phase with boreal summer insolation. Oscillations of shorter period are present in records from cores sited beneath the amplifier region. These occur between 12.5 and 74.5 Ka BP, when eccentricity modulation of precession is at a minimum. Within this time interval there are 21 cycles with mean periods of 3.0 plus or minus 0.5 Ky. Similar periods have been documented from high latitude regions, e.g., Greenland ice cores from Camp Century. The Camp Century signal in this same time interval contains 21 cycles. A subjective correlation was made between the Camp Century and the equatorial records; the signals were statistically similar, r = 0.722 and k = 0.960.
Deep learning (DL) has emerged as a promising tool for downscaling coarse-resolution climate data to high-resolution outputs, enabling improved regional climate predictions. A critical aspect of DL-based downscaling is the incorporation of uncertainty quantification (UQ), which enhances the interpretability and reliability of predictions—key factors for climate risk assessment and decision-making. This study develops a DL model to downscale 2 m temperature across the contiguous United States using reanalysis datasets. We systematically evaluate three epistemic UQ methods—deep ensembles (DEns), Monte Carlo dropout (MCD), and Flipout—based on their probabilistic accuracy, downscaling performance, sensitivity to geographical features, and computational efficiency. Results indicate that MCD generally outperforms Flipout and DEns in terms of calibration and downscaling accuracy. However, DEns demonstrate lower calibration errors in coastal regions, indicating its higher confidence within these areas. Flipout, in contrast, is more sensitive to elevation gradients and exhibits higher calibration errors in mountainous regions. Hence, the choice of UQ method for this task depends on the specific requirements of the application. For applications that prioritize overall calibration, downscaling accuracy, and computational efficiency, MCD is a strong candidate. These findings highlight the importance of selecting UQ methods based on application-specific requirements, such as geographical context and computational constraints. By addressing the trade-offs between UQ methods, this study provides actionable insights for improving the reliability, scalability, and utility of DL-based downscaling in climate science.
Papers are presented on the contribution of space remote sensing observations to the World Climate Research Program and the Global Change Program, covering topics such as space observations for global environmental monitoring, experiments related to land surface fluxes, studies of atmospheric composition, structure, motions, and precipitation, and remote sensing for oceanography, observational studies of the atmosphere, clouds, and the earth radiation budget. Also, papers are given on results from space observations for meteorology, oceanography, and mesoscale atmospheric and ocean processes. The topics include vertical atmospheric soundings, surface water temperature determination, sea level variability, data on the prehurricane atmosphere, linear and circular mesoscale convective systems, Karman vortex clouds, and temporal patterns of phytoplankton abundance.
Climate assessments consolidate our understanding of possible future climate conditions as represented by climate projections, which are largely based on the output of global climate models. Over the past 30 years, the scientific insights gained from climate projections have been refined through model structural improvements, emerging constraints on climate feedbacks, and increased computational efficiency. Within the same period, the process of assessing and evaluating information from climate projections has become more defined and targeted to inform users. As the size and audience of climate assessments has expanded, the framing, relevancy, and accessibility of projections has become increasingly important. This paper reviews the use of climate projections in national climate assessments (NCA) while highlighting challenges and opportunities that have emerged over time. Reflections and lessons learned address the continuous process to understand the broadening assessment audience and evolving user needs. Insights for future NCA development include (1) identifying benchmarks and standards for evaluating downscaled datasets, (2) expanding efforts to gather research gaps and user needs to inform how climate projections are presented in the assessment (3) providing practitioner guidance on the use, interpretation, and reporting of climate projections and uncertainty to better inform decision-making.
Aerosol–cloud interactions (ACIs) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty in estimating the ERF from aerosol–cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code. Most commonly used methods also cannot disentangle the contributions from different processes to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly averaged cloud fraction partitioned by cloud droplet effective radius (r e ) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed -0.34 ± 0.23, -0.22 ± 0.13, and -0.09 ± 0.11 W m -2 , respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS r e –LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.
Mitigating climate change in soil ecosystems involves complex plant and microbial processes regulating carbon pools and flows. Here, we advocate for the use of soil microbiome interventions to help increase soil carbon stocks and curb greenhouse gas emissions from managed soils. Direct interventions include the introduction of microbial strains, consortia, phage, and soil transplants, whereas indirect interventions include managing soil conditions or additives to modulate community composition or its activities. Approaches to increase soil carbon stocks using microbially catalyzed processes include increasing carbon inputs from plants, promoting soil organic matter (SOM) formation, and reducing SOM turnover and production of diverse greenhouse gases. Marginal or degraded soils may provide the greatest opportunities for enhancing global soil carbon stocks. Among the many knowledge gaps in this field, crucial gaps include the processes influencing the transformation of plant-derived soil carbon inputs into SOM and the identity of the microbes and microbial activities impacting this transformation. As a critical step forward, we encourage broadening the current widespread screening of potentially beneficial soil microorganisms to encompass functions relevant to stimulating soil carbon stocks. Moreover, in developing these interventions, we must consider the potential ecological ramifications and uncertainties, such as incurred by the widespread introduction of homogenous inoculants and consortia, and the need for site-specificity given the extreme variation among soil habitats. Incentivization and implementation at large spatial scales could effectively harness increases in soil carbon stocks, helping to mitigate the impacts of climate change.
The Physical Oceanography Distributed Active Archiving Center (PO.DAAC), NASA's primary data center for archiving and distributing oceanographic data, is supporting the Jason and TOPEX/Poseidon satellite tandem missions by providing a variety of data products, tools, and distribution methods to the wider scientific and general community. PO.DAAC has developed several new data products for sea level residual measurements, providing a longterm climate data record from 1992 to the present These products provide compatible measurements of sea level residuals for the entire time series including the tandem TOPEX/Poseidon and Jason mission. Several data distribution tool. are available from NASA PO.DAAC. The Near-Real-Time Image Distribution Server (NEREIDS) provides quicklook browse images and binary data files The PO.DAAC Ocean ESIP Tool (POET) provides interactive, on-tine data subsetting and visualization for several altimetry data products.
Abstract Global warming results from anthropogenic greenhouse gas emissions which upset the delicate balance between the incoming sunlight, and the reflected and emitted radiation from Earth. The imbalance leads to energy accumulation in the atmosphere, oceans and land, and melting of the cryosphere, resulting in increasing temperatures, rising sea levels, and more extreme weather around the globe. Despite the fundamental role of the energy imbalance in regulating the climate system, as known to humanity for more than two centuries, our capacity to observe it is rapidly deteriorating as satellites are being decommissioned.