In this study, we explore the impact of oceanic moisture fluxes on atmospheric blocks using the ECMWF Integrated Forecast System. Artificially suppressing surface latent heat flux over the Gulf Stream region leads to a significant reduction (up to 30%) in atmospheric blocking frequency across the northern hemisphere. Affected blocks show a shorter lifespan (-6%), smaller spatial extent (-12%), and reduced intensity (-0.4%), with an increased detection rate (+17%). These findings are robust across various blocking detection thresholds. Analysis indicates a resolution-dependent response, with resolutions lower than Tco639 (~18km) showing no significant change in some blocking characteristics, even with reduced blocking frequency. Exploring the broader Rossby wave pattern, we observe that diminished moisture flux favours eastward propagation and higher zonal wavenumbers, while air-sea interactions promotes stationary and westward-propagating waves with zonal wavenumber 3. This study underscores the critical role of western boundary current’s moisture fluxes in modulating atmospheric blocking.

Using nine years of full-depth profiles from 55 Deep Argo floats in the Southwest Pacific Basin collected between 2014 and 2023, we find consistent warm anomalies compared to a long-term climatology below 2000 m ranging between 11\(\pm\)2 to 34\(\pm\)2m\(^o\)C, most pronounced between 3500 and 5000 m. Over this period, a cooling trend is found between 2000-4000 m and a significant warming trend below 4000 m with a maximum rate of 4.1\(\pm\)0.31 m\(^o\)C yr\(^{-1}\) near 5000 m, with a possible acceleration over the second half of the period. The integrated Steric Sea Level expansion below 2000 m was 7.9\(\pm\) 1 mm compared to the climatology with a trend of 1.3\(\pm\) 1.6 mm dec\(^{-1}\) over the Deep Argo era, contributing significantly to the local sea level budget. We assess the ability to close a full Sea Level Budget, further demonstrating the value of a full-depth Argo array.

In this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a variable-resolution global atmospheric model, driven by two distinct sea surface temperature (SST) datasets: the 0.25° Optimum Interpolation Sea Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is assessed using a benchmarking framework, revealing good agreement between both simulations and the climatological rainfall spatial pattern, seasonality, and annual trends obtained from the Australian Gridded Climate Data (AGCD). Notably, wet biases are identified in both simulations, with CCAM_OISST displaying a more pronounced bias. Furthermore, we have examined CCAM’s ability to capture El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with rainfall during Austral spring (SON) utilizing a novel hit rate metric. Results indicate that only CCAM_OISST successfully replicates observed SON ENSO- and IOD-rainfall correlations, achieving hit rates of 86.6% and 87.5%, respectively, compared to 52.7% and 41.8% for CCAM_ERSST5. Large SST differences are found surrounding the Australian coastline between OISST and ERSST5 (termed the “Coastal Effect”). Differences can be induced by the spatial interpolation error due to the discrepancy between model and driving SST. An additional CCAM experiment, employing OISST with SST masked by ERSST5 in 5° proximity to the Australian continent, underscores the “Coastal Effect” has a significant impact on IOD-Australian rainfall simulations. In contrast, its influence on ENSO-Australian rainfall is limited. Therefore, simulations of IOD-Australian rainfall teleconnection are sensitive to local SST representation along coastlines, probably dependent on the spatial resolution of driving SST.

Reef-building corals provide seasonally resolved records of past climate variability from the ocean via variations in their oxygen isotope composition (δ18O). However, a variety of non-climatic factors can influence coral δ18O including processes associated with coral biomineralization and post-depositional alteration of the coral skeleton, which add uncertainty to coral based paleoclimate reconstructions. These uncertainties are especially large in mean climate reconstructions developed from coral δ18O values due to the large variability that exists in mean skeletal δ18O signatures. We present a novel framework to minimize this uncertainty in mean coral δ18O records based on a regression model that uses four commonly measured properties in coral skeletons and associated coral δ18O records. We test the ability of the model to reduce noise in a Holocene climate reconstruction comprised of 37 coral δ18O records from Kiritimati in the equatorial Pacific. Up to 43% of the variance in the detrended Holocene dataset is accounted for by a combination of four predictors: (1) mm-scale variability in a coral δ18O record, (2) the physical extent of diagenetic alteration, (3) coral extension rate, and (4) the mean coral δ13C value. Once these non-climatic artifacts are removed from the reconstruction, the weighted variance of the Holocene dataset is reduced by 46% and the uncertainty in the trend of coral δ18O over time is reduced by 26%. These results have important implications for the climate interpretation of this Holocene data set. This framework has the potential to improve other paleoclimate records based on ensembles of coral δ18O records.

As Earth warms, the tropopause is expected to rise, but predictions of its temperature change are less certain. One theory ties tropopause temperature to outgoing longwave radiation (OLR), but this contradicts simulations that exhibit a Fixed Tropopause Temperature (FiTT) even as OLR increases. Another theory ties tropopause temperature to upper tropospheric moisture, but is not precise enough to make quantitative predictions. Here, we argue that tropopause temperature, defined by where radiative cooling becomes negligible, is set by water vapor’s maximum spectroscopic absorption and Clausius-Clapeyron scaling. This “thermospectric constraint’ makes quantitative predictions for tropopause temperature that are borne out in single column and general circulation model experiments where the spectroscopy is modified and the tropopause changes in response. This constraint underpins the FiTT hypothesis, shows how tropopause temperature can decouple from OLR, suggests a way to relate the temperatures of anvil clouds and the tropopause, and shows how spectroscopy manifests in Earth’s general circulation.

As more countries make net zero greenhouse gas emissions pledges, it is crucial to understand the effects on global climate after achieving net zero emissions. The climate has been found to continue to evolve even after the abrupt cessation of CO2 emissions, with some models simulating a small warming and others simulating a small cooling. In this study, we analyse if the temperature and precipitation changes post abrupt cessation of CO2 emissions are significant compared to natural climate variations. We find that the temperature changes are outside of natural variability for most models, whilst the precipitation changes are mostly non-significant. We also demonstrate that post-net zero temperature changes have implications for the remaining carbon budget. The possibility of further global warming post-net zero adds to the evidence supporting more rapid emissions reductions in the near-term.

A document by Daniele Visioni. Click on the document to view its contents.

• Climate change and impacts will continue to accelerate until the warming influences are reduced or offset by direct cooling approaches. • Direct climate cooling approaches have the potential to reduce local to global portions of human-induced warming influences. • GHG emission reduction and removal policies alone will take at least decades to halt warming, much less restore 20 th century conditions.

• Deep depression occurrences have significantly increased over the eastern side, and decreased over the western side of the North Atlantic. • Deep depressions are linked to high surface temperature patterns in western continental Europe but have little impact on the mean warming.

We developed a coupled overland and river model for modeling compound flooding using the kinematic wave approximation on inland sections of an unstructured mesh, and the diffusive wave approximation on riverine sections. A finite element method is used for spatial discretization and a Crank-Nicolson scheme is used for time discretization. A wetting and drying algorithm is implemented for improved efficiency in the model. Pluvial conditions and tidal conditions are implemented as source terms in the river model. The results show that effects from compound inundation could be captured using this framework.

The atmospheric CO2 growth rate is a fundamental measure of climate forcing. NOAA's growth rate estimates, derived from in situ observations at the marine boundary layer (MBL), serve as the benchmark in policy and science. However, NOAA's MBL-based method encounters challenges in accurately estimating the whole-atmosphere CO2 growth rate at sub-annual scales. We introduce the Growth Rate from Satellite Observations (GRESO) method as a complementary approach to estimate the whole-atmosphere CO2 growth rate utilizing satellite data. Satellite CO2 observations offer extensive atmospheric coverage that extends the capability of the current NOAA benchmark. We assess the sampling errors of the GRESO and NOAA methods using ten atmospheric transport model simulations. The simulations generate synthetic OCO-2 satellite and NOAA MBL data for calculating CO2 growth rates, which are compared against the global sum of carbon fluxes used as model inputs. We find good performance for the NOAA method (R = 0.93, RMSE = 0.12 ppm/year or 0.25 PgC/year). GRESO demonstrates lower sampling errors (R = 1.00; RMSE = 0.04 ppm/year or 0.09 PgC/year). Additionally, GRESO shows better performance at monthly scales than NOAA (R = 0.77 vs 0.47, respectively). Due to CO2's atmospheric longevity, the NOAA method accurately captures growth rates over five-year intervals. GRESO's robustness across partial coverage configurations (ocean or land data) shows that satellites can be promising tools for low-latency CO2 growth rate information, provided the systematic biases are minimized using in situ observations. Along with accurate and calibrated NOAA in situ data, satellite-derived growth rates can provide information about the global carbon cycle at sub-annual scales.

A document by Indronil Sarkar. Click on the document to view its contents.

1 NASA Goddard Institute for Space Studies, New York, NY USA2 Dept. of Applied Physics and Applied Mathematics, Columbia Univ., New York, NY USA3 Dept. of Earth and Atmospheric Sciences, City College of New York, NY USA4 University of California, Los Angeles, Los Angeles, CA5 NASA Jet Propulsion Laboratory, Pasadena, CA USA* Corresponding author: Allegra N. LeGrande ([email protected] | [email protected] )

A document by Rodrigue Tanguy. Click on the document to view its contents.

A document by Rodrigue Tanguy. Click on the document to view its contents.

The effect of modified equator-to-pole temperature gradients on the jet stream by low-level polar warming and upper-level tropical warming on jet streams is not fully understood. We perform four aquaplanet simulations to quantify the impact of different sea surface temperature distributions on jet stream strength, wave amplitudes and jet stream waviness, quantified by a modified Sinuosity Index. A large-scale uniform warming scenario increases the jet strength whereas decreases in jet strength occur in two scenarios where the meridional temperature gradient is reduced. However, all scenarios indicate substantial decreases in the magnitude of large wave amplitudes, jet stream extreme waviness and reduced variability of these diagnostics, suggesting a relationship with weakened baroclinicity. Our findings contradict the earlier proposed mechanism that low-level polar warming weakens the jet stream and increases wave amplitudes and jet stream waviness. We conclude that a weaker jet stream does not necessarily become wavier.

Most studies focus on the impact of climate change on the mean state of phytoplankton and primary productivity, but little is known about whether and how climate change is impacting variance and extremes. In this study, we assess changes in chlorophyll-a concentration (CHL), which is an important proxy for primary production of marine ecosystems. Previous study suggests a decreasing variability in phytoplankton chlorophyll in both observational period and the model ensemble [1]. Our objective is to understand whether and how climate change impacts the whole distribution of variability in primary productivity. We utilize a series of statistical methods and reanalysis of CHL datasets to assess the variance and extremes of all distributions of CHL.

Proper Arctic sea-ice forecasting for the melt season is still a major challenge because of the recent lack of reliable pan-Arctic summer sea-ice thickness (SIT) data. A new summer CryoSat-2 SIT observation data set based on an artificial intelligence algorithm may alleviate this situation. We assess the impact of this new data set on the initialization of sea-ice forecasts in the melt seasons of 2015 and 2016 in a coupled sea ice-ocean model with data assimilation. We find that the assimilation of the summer CryoSat-2 SIT observations can reduce the summer ice-edge forecast error. Further, adding SIT observations to an established forecast system with sea-ice concentration assimilation leads to more realistic short-term summer ice-edge forecasts in the Arctic Pacific sector. The long-term Arctic-wide SIT prediction is also improved. In spite of remaining uncertainties, summer CryoSat-2 SIT observations have the potential to improve Arctic sea-ice forecast on multiple time scales.

Volcanic aerosol plumes over south east Asia (SEAsia), and only over SEAsia, have always been the trigger and sustaining cause of: El Niño Southern Oscillation (ENSO) events which are the dominant mode of variability in the global climate; Australian and Indonesian droughts; increased global temperatures; and Indian Ocean Dipole (IOD) events. In recent decades this natural plume has been augmented by an anthropogenic plume which has intensified these events especially from September to November. Understanding the mechanism which enables aerosols over SEAsia, and only over SEAsia, to create ENSO events is crucial to understanding the global climate. I show that the SEAsian aerosol plume causes ENSO events by: reflecting/absorbing solar radiation which warms the upper troposphere; and reducing surface radiation which cools the surface under the plume. This inversion reduces convection in SEAsia thereby suppressing the Walker Circulation and the Trade Winds which causes the Sea Surface Temperature (SST) to rise in the central Pacific Ocean and creates convection there. This further weakens/reverses the Walker Circulation driving the climate into an ENSO state which is maintained until the SEAsian aerosols dissipate and the climate system relaxes into a non-ENSO state. Data from the Global Volcanism Program (151 years), the Last Millennium Ensemble (1,156 years), MERRA-2 (41 years) and NASA MODIS on Terra (21 years) demonstrates this connection with the Nino 3.4 and 1+2 SST, the Southern Oscillation Index, and three events commonly associated with ENSO: drought in south eastern Australia; the IOD and a warmer World.

Eleven Earth System Models (ESMs) contributing to the Coupled Model Intercomparison Project (CMIP6) were evaluated with respect to climate-related stressors impacting Arctic marine ecosystems (temperature, sea ice, oxygen, ocean acidification). Stressors show regional differences and varying differences over time and space among models. Trend magnitudes increase over time and are highest by end-of-century for temperature and O2. Differences between scenarios SSP2-4.5 and SSP5-8.5 for these variables vary among models and regions, mainly driven by sea-ice retreat. Differences in biogeochemical parameterizations contribute to acidification differences. Projections indicate consistent ocean acidification until 2040 and faster progression for the higher emission scenario thereafter. For SSP5-8.5 all Arctic regions show aragonite undersaturation by 2080, and calcite undersaturation for all but two regions by 2100 for all models. Most regions can avoid calcite undersaturation with lower emissions (SSP2-4.5). All variables show increases in seasonal amplitude, most prominently for temperature and oxygen. Calcium carbonate saturation state (Ω) shows little change to the seasonal range and a suggestion of temporal shifts in extrema. Seasonal changes in Ω may be underestimated due to lacking carbon cycle processes within sea ice in CMIP6 models. The analysis emphasizes regionally varying threats from multiple stressors on Arctic marine ecosystems and highlights the propagation of uncertainties from sea ice to temperature and biogeochemical variables. Large model differences in seasonal cycles emphasize the need for improved model constraints, predominantly the representation of sea-ice decline, to enhance the applicability of CMIP models in multi-stressor impacts assessments.

Addressing impacts of flash droughts (FDs) on the water-food nexus requires a understanding of FD mechanisms and drivers at the watershed level. Examining climatic drivers, dry and wet spell lengths from 1980 to 2019, we analyzed FD spatial and temporal characteristics, emphasizing areal extent, onset time, and duration. Our findings reveal substantial variations in FDs among different watersheds. Notably, watersheds in the Southern Hemisphere are witnessing expanding, faster-developing, and longer-lasting FDs, aligning with climate variations in precipitation and temperature. Additionally, at the watershed scale, the onset and duration of FDs are influenced by climatic drivers but remain unaffected by the duration of wet and dry periods. FD extents, however, correlate with both climatic conditions and wet and dry periods, underscoring watershed connectivity. Ultimately, our results underscore the necessity for research to comprehend the interplay between FDs and watershed characteristics and how it manifests in overall water resource management.

Ocean acidification driven by the uptake of anthropogenic CO2 represents a major threat to ocean ecosystems, yet little is known about its progression beneath the surface. Here, we reconstruct the history of ocean interior acidification (OIA) from 1800 to 2014 on the basis of observation-based estimates of the accumulation of anthropogenic carbon. Across the top 100 m and over the industrial era, the saturation state of aragonite (Ωarag) and pH = -log[H+] decreased by more than 0.6 and 0.1, respectively, with a progress of nearly 50% over the last 20 years (1994-2014). While the magnitude of the Ωarag change decreases uniformly with depth, the magnitude of the pH decrease exhibits a distinct maximum in the upper thermocline. Since 1800, the saturation horizon (Ωarag=1) shoaled by more than 200 m, approaching the euphotic zone in several regions, especially in the Southern Ocean, and exposing many organisms to corrosive conditions.

Earth System Models (ESMs) are essential tools for understanding the interaction of the human and Earth systems. One key application of these models is studying extreme weather events, such as heat waves or high intensity precipitation events, which have significant socioeconomic consequences. However, the computational demands of running a sufficient number of simulations to robustly characterize expected changes in these hazards, and therefore provide a strong basis to analyze the ensuing risks, are often prohibitive. In this paper we demonstrate that diffusion models – a class of generative deep learning models – can effectively emulate the spatio-temporal trends of ESM daily output. Trained on a handful of runs, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input and is capable of producing daily values of temperature and precipitation that have statistical characteristics close to the ESM output. This approach requires only a small fraction of the computational resources that would be needed to run a large ensemble under any scenario of interest. We evaluate model behavior over a range of scenarios, time horizons and two ESMs, using a number of extreme metrics, including ones that have been long established in the climate modeling and analysis community. Our results show that the samples produced by DiffESM closely matches the spatio-temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity.

The interactions of clouds with radiation influence climate. Many of these impacts appear to be related to the radiative heating and cooling from high-level clouds in the upper troposphere, but few studies have explicitly tested this. Here, we use simulations with the ICON-ESM global atmosphere model to understand how high-level clouds through their radiative heating and cooling of the atmosphere, influence the large-scale atmospheric circulation and precipitation in the present-day climate. We introduce a new method to diagnose the radiative heating of high-level clouds: we use a temperature threshold of -35°C to define high-level clouds and also include the lower parts of these clouds at warmer temperatures. The inclusion of the lower cloud parts circumvents the creation of artificial cloud boundaries and strong artificial radiative heating at the temperature threshold. To isolate the impact of high-level clouds, we analyze simulations with active cloud-radiative heating, with the radiative heating from high-level clouds set to zero, and with the radiative heating from all clouds set to zero. We show that the radiative interactions of high-level clouds warm the troposphere and strengthen the eddy-driven jet streams, but have no impact on the strength of the Hadley circulation and the latitude of the Intertropical Convergence Zone. Consistent with their positive radiative heating and energetic arguments, high-level clouds reduce precipitation throughout the tropics and lower midlatitudes. Overall, our results confirm that the radiative interactions of high-level clouds have important impacts on climate and highlight the need for better representing their radiative interactions in models.