Anthropogenic climate change drives non-stationary phytoplankton internal variability

This study delves into the increasing extension of summer forest fires in California, primarily driven by anthropogenic climate change (Turco et al. 2023). Historical data indicate a fivefold increase in summer burned area (BA) in forests in northern and central California from 1996 to 2021 relative to 1971 to 1995. Using the latest simulations developed for climate change attribution and detection studies and accounting for the uncertainties arising from the data-driven climate-fire model, climate models, and internal climate variability, we have investigated the impact of anthropogenic climate change on the observed increase in BA in California’s forests. We detect the signal of combined natural and anthropogenic forcing on the observed BA starting in 2001 while finding the observed BA changes to be inconsistent with internal variability or natural forcing alone. We estimate that climate simulations that included both human and natural forcings yield 172% more BA from 1971 to 2021 than models without anthropogenic forcing, with a remarkable +320% increase from 1996 to 2021. Considering the significance of anthropogenic climate change for the rise in forest BA in California, we pose a crucial question: what will the future of fires look like with ongoing climate changes? Addressing this, we evaluate how fuel limitations resulting from fire-fuel feedbacks might alter future fire trajectories under the influence of anthropogenic climate change. Dynamic models incorporating various feedback strengths suggest an expected further increase in annual average forest BA, ranging from 3 to 52% compared to the mean of the last two decades (2001-2021), which also marks the highest 20-year records since 1971. This highlights the imperative for proactive adaptation strategies. Our findings underscore the urgent need to address the impacts of climate change within fire management and policymaking.

This paper introduces and applies a new method to consistently estimate internal climate variability for all models within a multimodel ensemble. The method regresses each model’s estimate of internal variability from the preindustrial control simulation on the variability derived from a model’s ensemble simulations, thus providing practical evidence of the quasi-ergodic assumption. The method allows one to test in a multimodel consensus view how the internal variability of a variable changes for different forcing scenarios. Applying the method to the CMIP5 model ensemble shows that the internal variability of global-mean surface air temperature remains largely unchanged for historical simulations and might decrease for future simulations with a large CO2forcing. Regionally, the projected changes reveal likely increases in temperature variability in the tropics, subtropics, and polar regions, and extremely likely decreases in midlatitudes. Applying the method to sea ice volume and area shows that their respective internal variability likely or extremely likely decreases proportionally to their mean state, except for Arctic sea ice area, which shows no consistent change across models. For the evaluation of CMIP5 simulations of Arctic and Antarctic sea ice, the method confirms that internal variability can explain most of the models’ deviation from observed trends but often not the models’ deviation from the observed mean states. The new method benefits from a large number of models and long preindustrial control simulations, but it requires only a small number of ensemble simulations. The method allows for consistent consideration of internal variability in multimodel studies and thus fosters understanding of the role of internal variability in a changing climate.

Due to its large heat capacity and circulation, the ocean contributes significantly to global heat uptake, global heat transport, spatial temperature patterns, and variability. Quantifying ocean heat uptake across different temporal and spatial scales is important to quantify Earth's climate response to anthropogenic warming. Here we evaluate ocean adjustment time scales from two different fully coupled climate model ensembles using the Community Earth System Model. Both ensembles use the same model version, anthropogenic and natural forcings, and coupling configurations, but we initialize the ensembles in two different ways: (1) sampling joint internal variability of the ocean–atmosphere system (unique atmosphere and ocean conditions) and (2) sampling the internal variability of the atmosphere only (unique atmosphere, identical ocean conditions). Uncertainty due to internal variability is used as a proxy to quantify the time scales of ocean temperature adjustment at different depths and basins in Community Earth System Model. Time scales of equilibration are longer in the deep ocean than the upper ocean, highlighting the vertical structure of dynamic adjustment. The Atlantic equilibrates on shorter time scales (82 years above 1,000 m, 140 years below 1,000 m) relative to the Pacific (106 years above 1,000 m, 444 years below 1,000 m) in Community Earth System Model due to the large North Atlantic Deep Water formation and strong overturning circulation in the Atlantic. These results have broad implications for analyzing internal climate variability, ocean adjustment, and drift in global coupled model experiments and intercomparisons.

Understanding what drives changes in heatwaves is imperative for all systems impacted by extreme heat. We examine short- (13 yr) and long-term (56 yr) heatwave frequency trends in a 21‐member ensemble of a global climate model (Community Earth System Model; CESM), where each member is driven by identical anthropogenic forcings. To estimate changes dominantly due to internal climate variability, trends were calculated in the corresponding pre-industrial control run. We find that short-term trends in heatwave frequency are not robust indicators of long-term change. Additionally, we find that a lack of a long-term trend is possible, although improbable, under historical anthropogenic forcing over many regions. All long-term trends become unprecedented against internal variability when commencing in 2015 or later, and corresponding short-term trends by 2030, while the length of trend required to represent regional long-term changes is dependent on a given realization. Lastly, within ten years of a short-term decline, 95% of regional heatwave frequency trends have reverted to increases. This suggests that observed short-term changes of decreasing heatwave frequency could recover to increasing trends within the next decade. The results of this study are specific to CESM and the ‘business as usual’ scenario, and may differ under other representations of internal variability, or be less striking when a scenario with lower anthropogenic forcing is employed.

Southeast Asian (SEA) river basins, particularly Huang He, Yangtze and Mekong river basins, are home for large share of global population. The region is experiencing rapid socio‐economic development, urbanization and industrialization. Using Community Earth System Model large ensemble data, seasonal mean surface air temperature (SAT) in summer is projected to increase by ~6°C under Representative Concentration Pathway (RCP) 8.5 scenario. As a consequence, in the long term (2071–2100), record‐breaking extreme hot and precipitation events are projected to increase by ~90–100% and ~ 60–70%, respectively. The climate mean SAT will increase by 4 ~ 5.5°C by the end of 21st century. Extreme events occur due to the combined influence of external forcing factors and internal variability. The relative strength of both the factors are estimated by signal‐to‐noise ratio (SNR), that is, the ratio of external forcings and internal variability. Until 2000/1998/2000, SNR < 1 over Huang He/Yangtze/Mekong river basins, that is, internal variability dominates over external forcings, while from 2001/1999/2001 onwards, SNR > 1, that is, external forcings strongly override the internal climate variability. Interestingly, the SAT variability at 50th (mean state) and 95th (record‐breaking hot/wet extreme) percentile will decrease significantly as climate warms in the future. It contradicts the existing hypothesis that as the climate warms in the future, variability will increase and weather will get more volatile everywhere. Interestingly, in our analysis, SAT variability appears to decrease over SEA river basins, which is opposite to the current understanding. It makes the result a novel one. We analyse the temperature–precipitation relationship under RCP8.5 scenario, which displays a peak‐shaped structure, that is, precipitation is increasing at low–mid‐range of temperature rise, while it decreases at higher temperatures.

The future evolution of sea surface temperature (SST) extremes is of great concern, not only for the health of marine ecosystems and sustainability of commercial fisheries, but also for precipitation extremes fueled by moisture evaporated from the ocean. This study examines the projected influence of anthropogenic climate change on the intensity and duration of monthly SST extremes, hereafter termed marine heat waves (MHWs) and marine cold waves (MCWs), based on initial-condition large ensembles with seven Earth system models. The large number of simulations (30–100) with each model allows for robust quantification of future changes in both the mean state and variability in each model. In general, models indicate that future changes in variability will cause MHW and MCW events to intensify in the northern extratropics and weaken in the tropics and Southern Ocean, and to shorten in duration in many areas. These changes are generally symmetric between MHWs and MCWs, except for the longitude of duration change in the tropical Pacific and sign of duration change in the Arctic. Projected changes in ENSO account for a large fraction of the variability-induced changes in MHW and MCW characteristics in each model and are responsible for much of the intermodel spread as a result of differences in future ENSO behavior. The variability-related changes in MHW and MCW characteristics noted above are superimposed upon large mean-state changes. Indeed, their contribution to the total change in SST during MHW and MCW events is generally <10% except in polar regions where they contribute upward of 50%.

Changes in the global water cycle are expected as a result of anthropogenic climate change, but large uncertainties exist in how these changes will be manifest regionally. This is especially the case over the tropical oceans, where observed estimates of precipitation and evaporation disagree considerably. An alternative approach is to examine changes in near-surface salinity. Datasets of observed tropical Pacific and Atlantic near-surface salinity combined with climate model simulations are used to assess the possible causes and significance of salinity changes over the late twentieth century. Two different detection methodologies are then applied to evaluate the extent to which observed large-scale changes in near-surface salinity can be attributed to anthropogenic climate change. Basin-averaged observed changes are shown to enhance salinity geographical contrasts between the two basins: the Pacific is getting fresher and the Atlantic saltier. While the observed Pacific and interbasin-averaged salinity changes exceed the range of internal variability provided from control climate simulations, Atlantic changes are within the model estimates. Spatial patterns of salinity change, including a fresher western Pacific warm pool and a saltier subtropical North Atlantic, are not consistent with internal climate variability. They are similar to anthropogenic response patterns obtained from transient twentieth- and twenty-first-century integrations, therefore suggesting a discernible human influence on the late twentieth-century evolution of the tropical marine water cycle. Changes in the tropical and midlatitudes Atlantic salinity levels are not found to be significant compared to internal variability. Implications of the results for understanding of the recent and future marine tropical water cycle changes are discussed.

The burden of El Niño-Southern Oscillation-related dengue attributable to anthropogenic climate change: a multicountry modelling study.

We employ a climate-driven hydrological water balance and sediment transport model (HydroTrend) to simulate future climate-driven sediment loads flowing into the Ganges-Brahmaputra-Meghna (GBM) mega-delta. The model was parameterised using high-quality topographic data and forced with daily temperature and precipitation data obtained from downscaled Regional Climate Model (RCM) simulations for the period 1971-2100. Three perturbed RCM model runs were selected to quantify the potential range of future climate conditions associated with the SRES A1B scenario. Fluvial sediment delivery rates to the GBM delta associated with these climate data sets are projected to increase under the influence of anthropogenic climate change, albeit with the magnitude of the increase varying across the two catchments. Of the two study basins, the Brahmaputra's fluvial sediment load is predicted to be more sensitive to future climate change. Specifically, by the middle part of the 21(st) century, our model results suggest that sediment loads increase (relative to the 1981-2000 baseline period) over a range of between 16% and 18% (depending on climate model run) for the Ganges, but by between 25% and 28% for the Brahmaputra. The simulated increase in sediment flux emanating from the two catchments further increases towards the end of the 21(st) century, reaching between 34% and 37% for the Ganges and between 52% and 60% for the Brahmaputra by the 2090s. The variability in these changes across the three climate change simulations is small compared to the changes, suggesting they represent a significant increase. The new data obtained in this study offer the first estimate of whether and how anthropogenic climate change may affect the delivery of fluvial sediment to the GBM delta, informing assessments of the future sustainability and resilience of one of the world's most vulnerable mega-deltas. Specifically, such significant increases in future sediment loads could increase the resilience of the delta to sea-level rise by giving greater potential for vertical accretion. However, these increased sediment fluxes may not be realised due to uncertainties in the monsoon related response to climate change or other human-induced changes in the catchment: this is a subject for further research.

In recent years, European summer heatwaves have strongly intensified due to rising anthropogenic emissions. While European summer heatwaves will continue to intensify due to the warming of summer temperatures, the effects of the changes in internal variability under global warming remain unknown. Employing five single-model initial-condition large ensembles, we find that the forced changes in internal variability are projected to intensify central and northern European summer heatwaves. Central and northern Europe will experience frequent moisture limitations, enhancing land-atmosphere feedback and increasing heatwave intensity and variability. In contrast, the forced changes in internal variability will contribute to weakening southern European summer heatwaves. Southern Europe is projected to face a more stable moisture-depleted environment that reduces extreme temperature variability and heatwave intensity. Our findings imply that while adaptation to increasing mean temperatures in southern Europe should suffice to reduce the vulnerability to increasing EuSHW intensity, in central and northern Europe adaptation to increased temperature variability will also be needed.

Changes in extreme weather events are a potentially important aspect of anthropogenic climate change (ACC), yet they are difficult to attribute to ACC because the record length is often similar to, or shorter than, extreme-event return periods. This study is motivated by the “World Weather Attribution” (WWA) initiative and, specifically, their approach of fitting extreme value distribution functions to local observations. They calculate the dependence of distribution parameters on global mean surface temperature (GMST) and use this dependence to attribute extreme events to ACC. Applying this method to preindustrial climate simulations with no time-varying greenhouse gas forcing, we still find a strong dependence of distribution parameters on GMST. This dependence results from internal climate variability (e.g., ENSO) affecting both extreme events and GMST. Therefore, dependence on GMST does not necessarily imply an effect of ACC on extremes. We further consider whether an extreme value, normal, or lognormal distribution better represents the data, if a GMST dependence of distribution parameters is justified using a likelihood ratio test, and if a meaningful attribution is possible given uncertainties in GMST dependence. We find, for example, that an attribution of Australia’s 2020–21 bushfires to ACC is difficult due to the effects of internal variability. For the 2019–21 drought in Madagascar, we find that the small number of available data points precludes a meaningful attribution analysis. Overall, we find that the effects of internal climate variability on GMST and the uncertain relationship between GMST and regional extremes may lead to inaccurate attribution conclusions using the part of the WWA approach examined here.

Mid-latitude storms are essential features of atmospheric variability in the cold season. The subsequent damages are caused by high wind speeds and heavy precipitation. Among such events, explosive cyclones can lead to extreme impacts when they make landfall. Climate change is affecting the underlying characteristics of such types of extremes. Being able to understand the way it modifies their dynamics is of great importance. In this work, we assess the influence of anthropogenic climate change on observed explosive cyclones in an Extreme Event Attribution framework using a large ensemble dataset. We evaluate three storms that hit different parts of Europe: Xynthia in February 2010, Alex in October 2020, and Eunice in January 2022. We use three ensembles of 35 members of the Community Earth System Model (CESM). We compare two periods of 6-hourly data: present-day climate [1991-2001] and future climate [RCP8.5 scenario, 2091–2101]. We find analogues of the trajectories of the three storms before their highest intensity in both periods. We do that by tracking all cyclones in the dataset and selecting the cyclone tracks that have the minimum Euclidean distance in km from the trajectories of Xynthia, Alex, and Eunice. We explore the characteristics of the analogues of the trajectories in both periods such as frequency of explosive cyclogenesis and intensity to evaluate whether the dynamics of the storms have been affected by climate change. We further compare the analogues in terms of precipitation and low-level wind in the regions of impact.

Projection of future extreme meteorological droughts using two large multi-member climate model ensembles

<p>Groundwater level (GWL) variations can be expressed over a wide range of timescales. As aquifers act as low-pass filters, low-frequency variability (interannual to decadal variability) originating from large-scale climate variability represents a significant part of GWL variance. Anthropogenically-driven climate change may affect, and have maybe already affected, the internal climate variability which explains the low-frequency variability of hydrological processes. Such changes in internal climate variability could therefore affect GWL variations. How GWL, including extremes, may respond to such changes and variations in climate variability however remains an open question.</p><p> </p><p>To tackle this issue, we implemented an empirical numerical approach allowing to assess the sensitivity of aquifers to changes in large-scale climate variability, using the whole Seine hydrosystem (76000 km<sup>2</sup>) as a case study. The approach consisted in: i) identifying and modifying the spectral content of precipitation, originating from large-scale climate variability, using signal processing; ii) injecting perturbed precipitation fields as input in a physically-based hydrological/hydrogeological model (the CaWaQS software) for the Seine river basin<em> </em>for simulating perturbed GWL; iii) comparing the spectral content, trend and extremes of perturbed GWL with the reference GWL<em>.</em> We used the Safran precipitation field over the period 1970-2018, which was initially used for model calibration and validation. GWL data for the Seine basin is a subset of a database of climate-sensitive time series (i.e. low anthropogenic influence) recently set up at the BRGM and University of Rouen Normandy. First, the Safran reanalysis and observed GWL time series were analyzed using continuous wavelet transform to identify the different timescales of variability: interannual (2-4yr and 5-8yr) and decadal (~15yr). Then, the different timescale of precipitation time series were extracted using maximum overlap discrete wavelet transform. For each time series of the precipitation field, the amplitude of each timescale was modified individually, by either increasing or decreasing it by 50%. This led to six scenarios of perturbed low-frequency variability of precipitation, which are subsequently used as input in the CaWaQS model to assess the response of GWL variability and extremes.</p><p> </p><p>Preliminary results indicate that perturbations of the amplitude of interannual to decadal precipitation variability result in substantial changes in the variability of GWL, affecting the same timescales, as well as timescales that were not modified in the precipitation field. Implications of these findings on potential trends and the frequency of extremes of GWL is currently being explored.</p>

Internal variability is the dominant cause of projection uncertainty of Arctic sea ice in the short and medium term. However, it is difficult to determine the realism of simulated internal variability in climate models, as observations only provide one possible realization while climate models can provide numerous different realizations. To enable a robust assessment of simulated internal variability of Arctic sea ice, we use a resampling technique to build synthetic ensembles for both observations and climate models, focusing on interannual variability, which is the dominant time scale of Arctic sea ice internal variability. We assess the realism of the interannual variability of Arctic sea ice cover as simulated by six models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) that provide large ensembles compared to four observational datasets. We augment the standard definition of model and observational consistency by representing the full distribution of resamplings, analogous to the distribution of variability that could have randomly occurred. We find that modeled interannual variability typically lies within observational uncertainty. The three models with the smallest mean state biases are the only ones consistent in the pan-Arctic for all months, but no model is consistent for all regions and seasons. Hence, choosing the right model for a given task as well as using internal variability as an additional metric to assess sea ice simulations is important. The fact that CMIP5 large ensembles broadly simulate interannual variability consistent within observational uncertainty gives confidence in the internal projection uncertainty for Arctic sea ice based on these models. Significance Statement The purpose of this study is to evaluate the historical simulated internal variability of Arctic sea ice in climate models. Determining model realism is important to have confidence in the projected sea ice evolution from these models, but so far only mean state and trends are commonly assessed metrics. Here we assess internal variability with a focus on the interannual variability, which is the dominant time scale for internal variability. We find that, in general, models agree well with observations, but as no model is within observational uncertainty for all months and locations, choosing the right model for a given task is crucial. Further refinement of internal variability realism assessments will require reduced observational uncertainty.