Our water resources have changed over the last century through a combination of water management evolutions and climate change. Understanding and decomposing these drivers of discharge changes is essential to preparing and planning adaptive strategies. We propose a methodology combining a physical-based model to reproduce the natural behavior of river catchments and a parsimonious model to serve as a framework of interpretation, comparing the physical-based model outputs to observations of discharge trends. We show that over Europe, especially in the South, the dominant explanations for discharge trends are non-climatic factors. Still, in some catchments of Northern Europe, climate change seems to be the dominating driver of change. We hypothesize that the dominating non-climatic factors are irrigation development, groundwater pumping and other human water usage, which need to be taken into account in physical-based models to understand the main drivers of discharge and project future changes.

A document by Ehsan Erfani. Click on the document to view its contents.

A document by Ehsan Erfani. Click on the document to view its contents.

A document by Alamgir Hossan. Click on the document to view its contents.

Peatlands are fundamental deposits of organic carbon. Thus, their protection is of crucial importance to avoid emissions from their degradation. Peat is a mixture of organic soil that originates from the accumulation of wetland plants under continuous or cyclical anaerobic conditions for long periods. Hence, a precise quantification of peat deposits is extremely important; for that, remote- and proximal-sensing techniques are excellent candidates. Unfortunately, remote-sensing can provide information only on the few shallowest centimeters, whereas peatlands often extend to several meters in depth. In addition, peatlands are usually characterized by difficult (flooded) terrains. So, frequency-domain electromagnetic instruments, as they are compact and contactless, seem to be the ideal solution for the quantitative assessment of the extension and geometry of peatlands. Generally, electromagnetic methods are used to infer the electrical resistivity of the subsurface. In turn, the resistivity distribution can, in principle, be interpreted to infer the morphology of the peatland. Here, to some extent, we show how to shortcut the process and include the expectation and uncertainty regarding the peat resistivity directly into a probabilistic inversion workflow. The present approach allows for retrieving what really matters: the spatial distribution of the probability of peat occurrence, rather than the mere electrical resistivity. To evaluate the efficiency and effectiveness of the proposed probabilistic approach, we compare the outcomes against the more traditional deterministic fully nonlinear (Occam's) inversion and against some boreholes available in the investigated area.  

Climate change is posing unprecedented challenges, necessitating the development of effective climate adaptation. Conventional computational models of climate adaptation frameworks inadequately account for our capacity to learn, update, and enhance decisions as exogenous information is collected. Here we investigate the potential of reinforcement learning (RL), a machine learning technique that exhibits efficacy in acquiring knowledge from the environment and systematically optimizing dynamic decisions, to model and inform adaptive climate decision-making. To illustrate, we derive adaptive stratigies for coastal flood protections for Manhattan, New York City, considering continuous observations of sea-level rise throughout the 21st century. We find that, when designing adaptive seawalls to protect Manhattan, the RL-derived strategy leads to a significant reduction in the expected cost, 6% to 36% under the moderate emissions scenario SSP2-4.5 (9% to 77% under the high emissions scenario SSP5-8.5), compared to previous methods. When considering multiple adaptive policies (buyout, accommodate, and dike), the RL approach leads to a further 5% (15%) reduction in cost, showcasing RL’s flexibility in addressing complex policy design problems when multiple policies interact. RL also outperforms conventional methods in controlling tail risk (i.e., low probability, high impacts) and avoiding losses induced by misinformation (e.g., biased sea-level projections), demonstrating the importance of systematic learning and updating in addressing extremes and uncertainties related to climate adaptation. The analysis also reveals that, given the large uncertainty and potential misjudgment about climate projection, “preparing for the worst” is economically more beneficial when adaptive strategies, such as those supported by the RL approach, are applied.

The Ocean Heat Content (OHC) anomaly has become an increasingly important climate parameter for the Intergovernmental Panel on Climate Change (IPCC) assessment and evaluation of climate change. One good reason is that the OHC appears to be less prone to climate variability, typically experienced with surface temperature and other climate parameters. Therefore, a reasonable estimate of OHC increase is important for research and climate related policies. Levitus et al. (2012) (https://doi.org/10.1029/2012GL051106) is a relevant ocean heat content related paper, and their analysis and estimate of OHC increase between 1955 and 2010 is high, about four to seven times greater than what the law of conservation of energy may allow. The source of discrepancy is analyzed in this commentary and it appears to be a result of using corrected ocean data sources. Therefore, verification of the observed increase in OHC using alternative ocean data sources is recommended.

Today the world is tackling climate change. The global threat of energy poverty along with the growing need for energy has escalated this crisis. The promotion of renewable energy sources is widely known as the main solution to this challenge. Many International and regional agreements address various aspects of renewable energy development such as trade, transit, security, and investment. Foreign investment is recognized as a crucial prerequisite for the global deployment of renewable energy since not all States have the financial and technological potential to develop this sector. Various investment agreements are signed to facilitate and promote investments. These instruments contain a mixture of obligations that have direct or indirect effects. Expropriation provisions which are often crystallized in the form of 'a duty not to expropriate' are among these obligations. This article analytically describes the legal aspects of this standard and proposes the trends that can better protect the foreign investments in this sector; a factor without which the foreign investors would normally be reluctant to invest. It concludes that restricted police power, guarantees of transfer, and a full compensation standard that entails the payment of compound interest are the prominent legal features that can best perform this task.

It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence (AI) algorithms, however, is challenged with its vagueness, non-quantitiveness, computational inefficiency, etc. During the past 18 years, these challenges have been essentially resolved, with the establishment of a rigorous formalism of causality analysis initially motivated from atmospheric predictability. This not only opens a new field in the atmosphere-ocean science, namely, information flow, but also has led to scientific discoveries in other disciplines, such as quantum mechanics, neuroscience, financial economics, etc., through various applications. This note provides a brief review of the decade-long effort, including a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications in geoscience pertaining to this journal, such as those on the anthropogenic cause of global warming, the decadal prediction of El Niño Modoki, the forecasting of an extreme drought in China, among others.

The higher-order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models. This study investigates an underexplored aspect of CLUBB: its capacity to simulate near-surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux. Using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB’s stability-driven substepping performed at each atmospheric time step. The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB-computed zonal and meridional wind speed tendencies. Our 30-year present-day climate simulations (1980-2010) show that static coupling overestimates 10-m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10-m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind-speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near-surface wind speeds and associated turbulent momentum transport structure. Notably, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns.

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.

Previous modeling studies indicate that even though marine cloud brightening under a susceptibility-based strategy is effective in reducing the global average surface temperature, it triggers a La Niña-like sea-surface temperature response with cooling mostly confined within lower latitudes. Here we explore a different cloud seeding strategy involving seeding of regions with low susceptibility. Simulations with the Community Earth System Model, version 2 (CESM2) reveal that because the regional forcing is weaker and more widespread, cooling is more evenly distributed over the globe. This new strategy also does not result in the La Niña-like state seen in the other strategies.

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.

A document by Ayako Yamamoto. Click on the document to view its contents.

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.