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Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. This paper presents a new technique of combining the driving force of a time series obtained using the Slow Feature Analysis (SFA) approach, then introducing the driving force into a predictive model to predict non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

As part of the terrestrial branch of the Japan-funded Arctic Climate Change Research Project (GRENE-TEA), which aims to clarify the role and function of the Arctic terrestrial system in the climate system, and assess the influence of its changes on a global scale, this model intercomparison project (GTMIP) is planned and being conducted to (1) enhance communication and understanding between the "minds and hands" (i.e., between the modelling and field scientists) and (2) assess the uncertainty and variations stemming from variability in model implementation/design and in model outputs due to climatic and historical conditions in the Arctic terrestrial regions. This paper provides an overview and the experiment protocol of Stage 1 of the project, site simulations driven by statistically fitted data created using the GRENE-TEA site observations for the last three decades. The target metrics for the model evaluation cover key processes in both physics and biogeochemistry, including energy budgets, snow, permafrost, phenology, and carbon budgets. The preliminary results on four metrics (annual mean latent heat flux, annual maximum snow depth, gross primary production, and net ecosystem production) already demonstrate the range of variations in reproducibility among existing models and sites. Full analysis on annual as well as seasonal time scales, to be conducted upon completion of model outputs submission, will delineate inter-dependence among the key processes, and provide the clue for improving the model performance. [ABSTRACT FROM AUTHOR]

A climate system which is transitioning from one state to another is known as an abrupt climate change. Most of the recent studies regarding abrupt climate change have focused on the changes occurring before and after the abrupt change point, while little attention has been given to the "transition process" which occurs when the system breaks away from the original state to a new state. In this study, a novel method for analyzing the process of abrupt climate change was presented. By using the mathematical model based on the Logistic model, the process of the abrupt change could be analyzed and divided into different phases which include start moment, end moment, stable state, and unstable transition state. Meanwhile, the method was confirmed to be effective by testing in a study of Pacific decadal oscillation (PDO) time sequence, and the results of this study specify that this abrupt change process of PDO has a relationship with global warming. [ABSTRACT FROM AUTHOR]

Models used to investigate impacts of climatic changes on spatio-temporal vegetation dynamics need to balance required accuracy with computational feasibility. To enhance the computational efficiency of these models, upscaling methods are required that maintain key fine-scale processes influencing vegetation dynamics. In this paper, an adjustable method – the dynamic two-layer classification concept (D2C) – for the upscaling of time- and space-discrete models is presented. D2C aims to separate potentially repetitive calculations from those specific to single grid cells. The underlying idea is to extract processes that do not require information about a grid cell's neighbourhood to a reduced-size non-spatial layer, which is dynamically coupled to the original two-dimensional layer. The size of the non-spatial layer is thereby adaptive and depends on dynamic classifications according to pre-specified similarity criteria. I present how D2C can be used in a model implementation on the example of TreeMig-2L, a new, efficient version of the intermediate-complexity forest-landscape model TreeMig. To discuss the trade-off between computational expenses and accuracy, as well as the applicability of D2C, I compare different model stages of TreeMig-2L via simulations of two different application scenarios. This comparison of different model stages demonstrates that applying D2C can strongly reduce computational expenses of processes calculated on the new non-spatial layer. D2C is thus a valuable upscaling method for models and applications in which processes requiring information about the neighbourhood constitute the minor share of the overall computational expenses. [ABSTRACT FROM AUTHOR]

Climate change impacts on the marine biogeochemistry and lower trophic level dynamics in the North Sea and Baltic Sea have been assessed using regional downscaling in a number of recent studies. However, most of these where only forced by physical conditions from Global Climate Models (GCMs) and regional downscaling considering the climate change impact on oceanic nutrient conditions from Global Earth System Models (ESMs) are rare and so far solely based on CMIP3-generation climate models. The few studies published show a large range in projected future primary production and hydrodynamic condition. With the addition of CMIP5 models and scenarios, the demand to explore the uncertainty in regional climate change projections increased. Moreover, the question arises how projections based on CMIP5-generation models compare to earlier projections and multi-model ensembles comprising both AR4 and AR5 generation forcing models. Here, we investigated the potential future climate change impacts to the North Sea and the Baltic Sea ecosystem using a coherent regional downscaling strategy based on the regional coupled bio-physical model ECOSMO. ECOSMO was forced by output from different ESMs from both CMIP3 and CMIP5 models. Multi-model ensembles using CMIP3/A1B and CMIP5/RCP4.5 scenarios are examined, where the selected CMIP5 models are the successors of the chosen CMIP3 models. Comparing projected changes with the present day reference condition, all these simulations predicted an increase in Sea Surface Temperature (SST) in both North Sea and Baltic Sea, reduction in sea ice in the Baltic, decrease in primary production in the North Sea and an increase in primary production in the Baltic Sea. Despite these largely consistent results on the direction of the projected changes, our results revealed a broad range in the amplitude of projected climate change impacts. Our study strengthens the claim that the choice of the ESM is a major factor for regional climate projections. The change in oceanic nutrient input appeared to be the major driver for the projected changes in North Sea primary production. Assessing the spread in ensemble groups, we found that there is for the North Sea a significant reduction in the spread of projected changes among CMIP5 forced model simulations compared to those forced by CMIP3 ESMs, except for salinity. The latter was due to an unexpected salinification observed in one of the CMIP5 model while all other models exhibit freshening in the future. However, for the Baltic Sea substantial differences in inter-model variability in projected climate change impact to primary production is lacking. [ABSTRACT FROM AUTHOR]

The ammonia cycle and nitrate particle formation have been introduced in the LMDz- INCA global model. Both fine nitrate particles formation in the accumulation mode and coarse nitrate forming on existing dust and sea-salt particles are considered. The model simulates distributions of nitrates and related species in agreement with previous studies and observations. The calculated present-day total nitrate direct radiative forcing since the pre-industrial is -0.056Wm-2. This forcing has the same magnitude than the forcing associated with organic carbon particles and represents 18% of the sulfate forcing. Fine particles largely dominate the nitrate forcing representing close to 90% of this value. The model has been used to investigate the future changes in nitrates and direct radiative forcing of climate based on snapshot simulations for the four Representative Concentration Pathway (RCP) scenarios and for the 2030, 2050 and 2100 time horizons. Due to a decrease in fossil fuel emissions in the future, the concentrations of most of the species involved in the nitrate-ammonium-sulfate system drop by 2100 except for ammonia which originates from agricultural practices and for which emissions significantly increase in the future. Despite the decrease of nitrate surface levels in Europe and Northern America, the global burden of accumulation mode nitrates increases by up to a factor of 2.6 in 2100. This increase in nitrate in the future arises despite decreasing NOx emissions due to increased availability of ammonia to form ammonium nitrate. The total aerosol direct forcing decreases from its present-day value of -0.234Wm-2 to a range of -0.070 to -0.130Wm-2 in 2100 based on the considered scenario. The direct forcing decreases for all aerosols except for nitrates for which the direct negative forcing increases to a range of -0.060 to -0.115Wm-2 in 2100. Including nitrates in the radiative forcing calculations increases the total direct forcing of aerosols by a factor of 1.3 in 2000, by a factor of 1.7-2.6 in 2030, by 1.9-4.8 in 2050 and by 6.4-8.6 in 2100. These results show that agricultural emissions of ammonia will play a key role in the future mitigation of climate change with nitrates becoming the dominant contributor to the anthropogenic aerosol optical depth during the second half of the XXIst century and significantly increasing the calculated aerosol direct forcing. This significant increase in the influence that nitrate exert on climate in the future will at the same time affect regional air quality and nitrogen deposition to the ecosystems. [ABSTRACT FROM AUTHOR]

The article presents a study that demonstrates the applications of the Biogeochemical Transport and Reactions (CLM4-BeTR) in regional and global biogeochemistry modeling and climate predictability. It outlines the functions of the model in which its transport code are evaluated through several analytical test cases. The study shows that the method provides detailed comparison between ecosystem observation and predictions.

The article presents a study that investigates the effectiveness of Earth System Model (ESM) in determining carbon-cycle climate feedback and the future climate. It describes the method of the study that analyzes the paleoclimate experiments proposed by the Coupled Model Intercomparison Project. It notes the result of the study, which shows that the complexity of the model requires various steps to correctly configure the experiments.

Lake Ohrid (Macedonia/Albania) is a rare example of a deep, ancient Mediterranean lake and is a key site for palaeoclimate research in the northeastern Mediterranean region. This study conducts the first high-resolution diatom analysis during the Lateglacial and Holocene in Lake Ohrid. It demonstrates a complex diatom response to temperature change, with a direct response to temperature-induced productivity and an indirect response to temperature-related stratification/mixing regime and epilimnetic nutrient availability. During the Lateglacial (ca. 12 300-11 800 cal yr BP), the low-diversity dominance of hypolimnetic Cyclotella fottii indicates low temperature-dependent lake productivity. During the earliest Holocene (ca. 11 800-10 600 cal yr BP), although the slight increase in small, epilimnetic C. minuscula suggests climate warming and enhanced thermal stratification, diatom concentration remains very low as during the Lateglacial, indicating that temperature increase was muted. The early Holocene (ca. 10 600- 8200 cal yr BP) marked a sustained increase in epilimnetic taxa, with mesotrophic C. ocellata indicating high temperature-induced lake productivity between ca. 10 600- 10 200 cal yr BP and between ca. 9500-8200 cal yr BP, and with C. minuscula in response to low nutrient availability in the epilimnion between ca. 10 200-9500 cal yr BP. During the mid Holocene (ca. 8200-2600 cal yr BP), when sedimentological and geochemical proxies provide evidence for high temperature, anomalously low C. ocellata abundance is probably a response to epilimnetic nutrient limitation, almost mimicking the Lateglacial flora apart from mesotrophic Stephanodiscus transylvanicus indicative of high temperature-induced productivity in the hypolimnion. During the late Holocene (ca. 2600-0 cal yr BP), high abundance and fluctuating composition of epilimnetic taxa is largely a response to enhanced anthropogenic nutrient input. In this deep, oligotrophic lake, this study demonstrates the strong influence of lake physical and chemical processes in mediating the complex response of diatoms to climate change with particular respect to temperature. [ABSTRACT FROM AUTHOR]

Coastal hypoxia, the depletion of oxygen concentration in coastal waters, is becoming more prominent on a global scale. Changes in climate and nutrient loadings are two aspects of global change that are expected to profoundly impact coastal hypoxia. We investigated the role of these drivers on the evolution of hypoxia in a stratified, temperate coastal system using a one-dimensional model. The model couples three submodels, describing the physical characteristics, the pelagic ecosystem and benthic diagenesis. The model is calibrated for the Central North Sea but the model approach is generic, and can be applied in stratified coastal ecosystems. Our results indicate that the projected changes in climatological conditions for the North Sea over the next 100 yr will increase the risk of hypoxia. On average the oxygen concentration is predicted to decrease by 17 µM, mostly due to a reduced solubility at higher water temperature (responsible for 65% of the decrease). Increased stratification (22 %) and enhanced biological rates due to higher water temperature (13 %) also affect the future oxygen concentration. Nutrient loadings also have a strong effect on the occurrence of hypoxia. Decreasing nutrient concentrations strongly decreases the probability of hypoxic events, stressing the importance of continued extensive eutrophication management to mitigate the effect of increased temperature. [ABSTRACT FROM AUTHOR]

The article presents a study that demonstrates the usefulness of the Norwegian Climate Center's Earth System Model (NorESM1-M) in the coupled model intercomparison project phase 5 (CMIP5). It outlines the role of the model in providing complementary results to the evaluation of possible man made climate change. It also demonstrates the effectiveness of the model in accurately depicting the changes in the atmospheric water cycle in precipitation events.

The article presents a research which investigates the proposed framework for downscaling coarse climate change projections for oceans in the Australian region. It explores significance of the downscaling model in projecting marine impacts of climate change. The restoration of the ocean feedback on the air-sea fluxes to surface temperature and salinity is explored. It notes that the framework offers an attractive way to explore the impact of climate change on mesoscale ocean environment.

The North Atlantic is the most intense region of ocean CO2 uptake. Here, we investigate multidecadal timescale variability of the partial pressure CO2 (pCO2) that is due to the natural carbon cycle using a regional model forced with realistic climate and pre-industrial atmospheric pCO2 for 1948-2009. Large-scale patterns of natural pCO2 variability are primarily associated with basin-averaged sea surface temperature (SST) that, in turn, is composed of two parts: the Atlantic Multidecadal Oscillation (AMO) and a long-term positive SST trend. The North Atlantic Oscillation (NAO) drives a secondary mode of variability. For the primary mode, positive AMO and the SST trend modify pCO2 with different mechanisms and spatial patterns. Warming with the positive AMO increases subpolar gyre pCO2, but there is also a significant reduction of dissolved inorganic carbon (DIC) due primarily to reduced vertical mixing. The net impact of positive AMO is to reduce pCO2 in the subpolar gyre. Through direct impacts on SST, the net impacts of positive AMO is to increase pCO2 in the subtropical gyre. From 1980 to present, long-term SST warming has amplified AMO impacts on pCO2. [ABSTRACT FROM AUTHOR]

To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 µm AOD and 0.55/0.86 µm-based Ångstrom Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March-April-May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievibility through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS ? VIIRS aerosol CDR. [ABSTRACT FROM AUTHOR]

Ocean heat content varies on a range of timescales. Traditionally the atmosphere is seen to dominate the oceanic heat content variability. However, this variability can be driven either by oceanic or atmospheric heat fluxes. To diagnose the relative contributions and respective timescales, this study uses a box model forced with output from an ocean general circulation model (OGCM) to investigate the heat content variability of the upper 800m of the subtropical North Atlantic from 26° N to 36° N. The ocean and air-sea heat flux data needed to force the box model is taken from a 19 yr (1988 to 2006) simulation performed with the 1/12° version of the OCCAM OGCM. The box model heat content is compared to the corresponding heat content in OCCAM for verification. The main goal of the study is to identify to what extent the seasonal to interannual ocean heat content variability is of atmospheric or oceanic origin. To this end, the box model is subjected to a range of scenarios forced either with the full (detrended) ocean and air-sea fluxes, or their deseasoned counterparts. Results show that in all cases, the seasonal variability is dominated by the seasonal component of the air-sea fluxes, which produce a seasonal range in mean temperature of the upper 800m of ˜0.42 °C. However, on longer timescales oceanic heat transport dominates, with changes of up to ˜0.30 °C over 4 yr. The technique is subsequently applied to observational data. For the ocean heat fluxes, we use data from the RAPID program at 26° N from April 2004 to January 2011. At 36° N heat transport is inferred using a linear regression model based on the oceanic low-frequency transport in OCCAM. The air-sea flux from OCCAM is used for the period 2004 to 2006 when the RAPID timeseries and the OCCAM simulation overlap, and a climatology is used for the air-sea flux from 2006 onwards. The results confirm that on longer (>2 yr) timescales the ocean dominates the ocean heat content variability, which is further verified using data from the ARGO project. This work illustrates that oceanic divergence significantly impacts the ocean heat content variability on timescales relevant for applications such as seasonal hurricane forecasts. [ABSTRACT FROM AUTHOR]

The article discusses the Hadley Centre Global Environment Model version 2 (HadGEM2) family of climate configurations that comprises a range of specific model configurations. It offers a detailed descriptions and evaluations of HadGEM2 family members and its overall performance using a set of model metrics. It notes that HadGEM2 family of configurations includes atmosphere and ocean components with well-resolved stratosphere and earth-system.

Non-equilibrium is dominant in geophysical and climate phenomena. However the study of non-equilibrium is much more difficult than equilibrium and the relevance of probabilistic simplified models has been emphasized. Large deviation rates have been used recently in climate science. In this paper after recalling progress during the last decades in understanding the role of large deviations in a class of non-equilibrium systems we point out differences between equilibrium and non-equilibrium. For example in non-equilibrium: a) large deviation rates may be extensive but not simply additive. b) In non-equilibrium there are generically long range space correlations so large deviation rates are non-local. c) Singularities in large deviation rates denote the existence of phase transitions often not possible in equilibrium. To exemplify we shall refer to lattice gas models like the symmetric simple exclusion process and other models which are playing an important role in the understanding of non-equilibrium physics. Reasons why all this may be of interest in climate physics will be briefly indicated. [ABSTRACT FROM AUTHOR]

Wetland loss and climate change are known to alter regional and global methane (CH4) budgets. Over the last six decades, an extensive area of marshland has been converted to cropland on the Sanjiang Plain in Northeast China, and a significant increase in air temperature has also been observed there, while the impacts on regional CH4 budgets remain uncertain. Through model simulation, we estimated the changes in CH4 emissions associated with the conversion of marshland to cropland and climate change in this area. Model simulations indicated a significant reduction of 1.1 Tgyr-1 from the 1950s to the 2000s in regional CH4 emissions. The cumulative reduction of CH4 from 1960 to 2009 was estimated to be ~ 36Tg relative to the 1950s, and marshland conversion and the climate contributed 86% and 14% of this change, respectively. Interannual variation in precipitation (linear trend with P > 0.2) contributed to yearly fluctuations in CH4 emissions, but the relatively lower amount of precipitation over the period 1960-2009 (47 mmyr-1 lower on average than in the 1950s) contributed ~91% of the reduction in the area-weighted CH4 flux. Global warming at a rate of 0.3 °C per decade (P < 0.001) has increased CH4 emissions significantly since the 1990s. Relative to the mean of the 1950s, the warming-induced increase in the CH4 flux has averaged 19 kg ha-1 yr-1 over the last two decades. For the RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 scenarios of the fifth IPCC assessment report (AR5), the CH4 flux is predicted to increase by 36 %, 52 %, 78% and 95 %, respectively, by the 2080s compared to 1961-1990 in response to climate warming and wetting. [ABSTRACT FROM AUTHOR]

The seasonal variability of surface layer salinity (SLS), evaporation (E), precipitation (P ) and E -P over the global ocean is examined using in situ salinity data and the National Center for Environmental Prediction's Climate System Forecast Reanalysis. Sea- 5 sonal amplitudes and phases are calculated using harmonic analysis and presented in all areas of the open ocean between 60° S and 60° N. Areas with large amplitude SLS seasonal variations include: the intertropical convergence zone in the Atlantic, Pacific and Indian Oceans; western marginal seas of the Pacific; and the Arabian Sea. The median value in areas that have statistically significant seasonal cycles of SLS 10 is 0.19. Between about 60° S and 60° N, 37% of the ocean surface has a significant seasonal cycle of SLS and 75% a seasonal cycle of E -P . Phases of SLS have a bimodal distribution, with most areas of the ocean peaking in SLS in either March/April or September/October. The same calculation is done with surface freshwater flux using a mixed-layer depth 15 climatology. With the exception of an area near the western boundaries of the North Atlantic and North Pacific, seasonal variability is dominated by precipitation. Surface freshwater fluxes also have a bimodal distribution, with peaks in January and July, 1-2 months before the peaks of SLS. The amplitudes and phases of SLS and surface fluxes compare well in a qualitative 20 sense, suggesting that much of the variability in SLS is due to E -P forcing. However, the amplitudes of SLS are somewhat larger than would be expected and the peak of SLS comes typically about one month earlier than expected. The differences of the amplitudes of the two quantities is largest in such areas as the Amazon River plume, the Arabian Sea, the ITCZ and the eastern equatorial Pacific and Atlantic, indicating 25 that other processes such as ocean mixing and lateral transport must be important, especially in the tropics. [ABSTRACT FROM AUTHOR]

The climate science community faces a major challenge with respect to communicating the risks associated with climate change within a heavily politicised landscape that is characterised by varying degrees of denial, scepticism, distrust in scientific enterprise, and an increased prevalence of misinformation ("fake news"). This issue is particularly significant given the reliance on conventional "deficit" communication approaches, which are based on the assumption that scientific information provision will necessarily lead to desired behavioural changes. Indeed, the constrained orthodoxy of scientific practices in seeking to maintain strict objectivity and political separation imposes very tangible limits on the potential effectiveness of climate scientists for communicating risk in many contemporary settings. To address these challenges, this paper uses insights from a collaboration between UK climate scientists and artist researchers to argue for a more creative and emotionally attentive approach to climate science engagement and advocacy. In so doing, the paper highlights innovative ways in which climate change communication can be reimagined through different art forms to enable complex concepts to become knowable. We suggest that in learning to express their work through forms of art, including print-making, theatre and performance, song-writing, and creative writing, researchers experienced not only a sense of liberation from the rigid communicative framework operating in their familiar scientific environment but also a growing self-confidence in their ability and willingness to engage in new ways of expressing their work. As such, we argue that scientific institutions and funding bodies should recognise the potential value of climate scientists engaging in advocacy through art–science collaborations and that these personal investments and contributions to science engagement by individuals should be rewarded and valued alongside conventional scientific outputs. [ABSTRACT FROM AUTHOR]

The basic features of the wave climate in the South-Eastern Baltic Sea are studied based on available long-term measurements and simulations. The analysis of average, typical and extreme wave conditions, frequency of occurrence of different wave parameters, variations 5 in wave heights from weekly to decadal scales, etc., is performed based on waverider measurements at the Darss Sill since 1991. The measured climatology is compared against numerical simulations with the WAM wave model driven by downscaled reanalysis of wind fields for 1958-2002 and by adjusted geostrophic winds for 1970-2007. The wave climate in this region is typical for semi-enclosed basins of 10 the Baltic Sea. The maximum wave heights are about half of those in the Baltic Proper. The overall reliably recorded maximum significant wave height HS = 4.46m occurred during a severe S-SW storm in 1993 when the 10-min average wind speed reached 28ms-1. The long-term average significant wave height (0.75 m) shows modest interannual (about 12% of the long-term mean) and substantial seasonal variation. The 15 wave periods are mostly concentrated in a narrow range of 2.5-4 s and their distribution is almost constant over decades. The role of remote swell is very small. The annual wave properties show large interannual variability but no long-term trends in average and extreme wave heights can be observed. [ABSTRACT FROM AUTHOR]

The 2014-2018 drought over South Africa's winter rainfall zone (WRZ) created a critical water crisis which highlighted the region's drought and climate change vulnerability. Consequently, it is imperative to better understand the climatic characteristics of the drought in order to inform regional adaptation to projected climate change. In this paper we investigate the spatio-temporal patterns of drought intensity and the recent rainfall trends, focusing on assessing the consistency of the prevailing conceptual model of drought drivers with observed patterns. For this we use the new spatial subdivision for the region encompassing the WRZ introduced in our companion paper (Conradie et al., 2022). Compared to previous droughts since 1979, the 2014-2018 drought in the WRZ core was characterised by a markedly lower frequency of very wet days (exceeding the climatological 99.5th percentile daily rainfall -- including dry days) and of wet months (SPI1 > 0:5), a sub-seasonal attribute not previously reported. There was considerable variability in the spatial footprint of the drought. Short-term drought began in the south-western core WRZ in spring 2014. The peak intensity gradually spread north-eastward, although a spatially near-uniform peak is seen during mid-2017. The overall drought intensity for the 2015--2017 period transitions radially from most severe in the WRZ core to least severe in the surroundings. During 2014 and 2015, the drought was most severe at those stations receiving the largest proportion of their rainfall from westerly and north-westerly winds; by 2018, those stations receiving the most rain from the south and south-east were most severely impacted. This indicates an evolving set of dynamic drivers associated with distinct rain-bearing synoptic flows. No evidence is found to support the suggestion that the drought was more severe in the mountain catchments of Cape Town's major supply reservoirs than elsewhere in the core nor that rain day frequency trends since 1979 are more negative in this subdomain. Rainfall and rain day trend rates also exhibit some connections to the spatial seasonality structure of the WRZ, although this is weaker than for drought intensity. Caution should be applied in assessing South African rain day trends given their high sensitivity to observed data shortcomings. Our findings suggest an important role for zonally asymmetric dynamics in the region's drought evolution. This analysis demonstrates the utility of the spatial subdivisions proposed in the companion paper by highlighting spatial structure in drought intensity evolution linked to rainfall dynamics. [ABSTRACT FROM AUTHOR]

The seasonal cycle is the mode that couples climate forcing to ecosystem production. A better understanding of the regional characteristics of the seasonal cycle addresses an important gap in our understanding of the sensitivity of the biological pump to climate change. The regional characteristics of the seasonal cycle of phytoplankton biomass in the Southern Ocean were examined in terms of the timing of the bloom initiation, its amplitude, regional scale variability and the importance of the climatological seasonal cycle in explaining the overall variance. The study highlighted important differences between the spatial distribution of satellite observed phytoplankton biomass and the more dynamically linked characteristics of the seasonal cycle. The seasonal cycle was consequently defined into four broad zonal regions; the subtropical zone (STZ), the transition zone (TZ), the Antarctic circumpolar zone (ACZ) and the marginal ice zone (MIZ). Defining the Southern Ocean according to the characteristics of its seasonal cycle provides a more dynamic understanding of ocean productivity based on underlying physical drivers rather than climatological biomass. The response of the biology to the underlying physics of the different seasonal zones resulted in an additional classification of four regions based on the extent of interannual seasonal phase locking and the amplitude of the integrated seasonal biomass. This characterisation contributes to an improved understanding of regional sensitivity to climate forcing potentially allowing more robust predictions of long term climate trends. [ABSTRACT FROM AUTHOR]

Weighted positive matrix factorization (PMF) has been used by scientists to find small sets of underlying factors in environmental data. However, as the size of the data has grown, increasing computational costs have made it impractical to use traditional methods for this factorization. In this paper, we present a new weighting method to dramatically decrease computational costs for these traditional algorithms. We then apply this weighting method with the Randomized Hierarchical Alternating Least Squares (RHALS) algorithm to a large environmental dataset, where we show that interpretable factors can be reproduced using these methods. We show this algorithm results in a computational speedup of 38, 67, and 634 compared to the Multiplicative Update (MU), deterministic Hierarchical Alternating Least Squares (HALS), and non-negative Alternating Least Squares (ALS) algorithms, respectively. We also investigate rotational ambiguity in the solution, and present a simple "pulling" method to rotate a set of factors. This method is shown to find alternative solutions, and in some cases, lower the weighted residual error of the algorithm. [ABSTRACT FROM AUTHOR]

Models of the carbon cycle and climate on geologic (>104 year) timescales have improved tremendously in the last 50 years due to parallel advances in our understanding of the Earth system and the increase in computing power to simulate its key processes. Despite these advances, balancing the Earth System's vast complexity with a model's computational expense is a primary challenge in model development. Running longer simulations spanning hundreds of thousands of years or more generally requires reducing the complexity of the modeled climate system. However, simpler model frameworks often leave out certain features of the climate system, such as radiative feedbacks, shifts in atmospheric circulation, and the expansion and decay of ice sheets, which can have profound effects on the long-term carbon cycle. Here, we present a model for climate and the long-term carbon cycle that captures many fundamental features of global climate while retaining the computational efficiency needed to simulate millions of years of time. The Carbon- H2O Coupled HydrOlOgical model with Terrestrial Runoff And INsolation, or CH2O-CHOO TRAIN, couples a one-dimensional (latitudinal) moist static energy balance model of climate with a model for rock weathering and the long-term carbon cycle. The key advantages of this framework are (1) it simulates fundamental climate forcings and feedbacks; (2) it accounts for geographic configuration; and (3) it is highly customizable, equipped to easily add features, change the strength of feedbacks, and prescribe conditions that are often hard-coded or emergent properties of more complex models, such as climate sensitivity and the strength of meridional heat transport. The CH2O-CHOO TRAIN is capable of running million-year-long simulations in about thirty minutes on a laptop PC. This paper outlines the model equations, presents a sensitivity analysis of the climate responses to varied climatic and carbon cycle perturbations, and discusses potential applications and next stops for the CH2O-CHOO TRAIN. [ABSTRACT FROM AUTHOR]

This article presents the results of a bifurcation analysis of a simple energy balance model (EBM) for the future climate of the Earth. The main focus is on the following question: can the nonlinear processes intrinsic to atmospheric physics, including natural positive feedback mechanisms, cause a mathematical bifurcation of the climate state, as a consequence of continued anthropogenic forcing by rising greenhouse gas emissions? Our analysis shows that such a bifurcation could cause an abrupt change to a drastically different climate state in the EBM, which is warmer and more equable than any climate existing on Earth since the Pliocene epoch. In previous papers, with this EBM adapted to paleoclimate conditions, it was shown to exhibit saddle-node and cusp bifurcations, as well as hysteresis. The EBM was validated by the agreement of its predicted bifurcations with the abrupt climate changes that are known to have occurred in the paleoclimate record, in the Antarctic at the Eocene–Oligocene transition (EOT) and in the Arctic at the Pliocene–Paleocene transition (PPT). In this paper, the EBM is adapted to fit Anthropocene climate conditions, with emphasis on the Arctic and Antarctic climates. The four Representative Concentration Pathways (RCP) considered by the IPCC (Intergovernmental Panel on Climate Change) are used to model future CO2 concentrations, corresponding to different scenarios of anthropogenic activity. In addition, the EBM investigates four naturally occurring nonlinear feedback processes which magnify the warming that would be caused by anthropogenic CO2 emissions alone. These four feedback mechanisms are ice–albedo feedback, water vapour feedback, ocean heat transport feedback, and atmospheric heat transport feedback. The EBM predicts that a bifurcation resulting in a catastrophic climate change, to a pre-Pliocene-like climate state, will occur in coming centuries for an RCP with unabated anthropogenic forcing, amplified by these positive feedbacks. However, the EBM also predicts that appropriate reductions in carbon emissions may limit climate change to a more tolerable continuation of what is observed today. The globally averaged version of this EBM has an equilibrium climate sensitivity (ECS) of 4.34 K, near the high end of the likely range reported by the IPCC. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts to better understand past, present and future climate changes in a multi-model context. A total of 21 Model Intercomparison Projects (MIPs) were endorsed in its 6th phase (CMIP6), which included 190 different experiments that were used to simulate 40000 years and produced around 40 PB of data in total. This paper shows the main results obtained from the collection of performance metrics done for CMIP6 (CPMIP). The document provides the list of partners involved, the CPMIP metrics per institution/model, and the approach used for the collection and the coordination behind this process. Furthermore, a section has been included to analyze the results and prove the usefulness of the metrics for the community. Moreover, we describe the main difficulties faced during the collection and propose recommendations for future exercises. [ABSTRACT FROM AUTHOR]

Even the most advanced climate models struggle to reproduce the observed wintertime circulation of the atmosphere over the North Atlantic and western Europe. During winter, the large-scale motions of this particularly challenging region are dominated by eddy-driven and highly non-linear flows, whose low-frequency variability is often studied from the perspective of regimes – a small number of qualitatively distinct atmospheric states. Poor representation of regimes associated with persistent atmospheric blocking events, or variations in jet latitude, degrades the ability of models to correctly simulate extreme events. In this paper we leverage a recently developed hybrid approach – which combines both jet and geopotential height data – to assess the representation of regimes in 8400 years of historical climate simulations drawn from the Coupled Model Intercomparison Project (CMIP) experiments, CMIP5, CMIP6, and HighResMIP. We show that these geopotential-jet regimes are particularly suited to the analysis of climate data, with considerable reductions in sampling variability compared to classical regime approaches. We find that CMIP6 has a considerably improved spatial regime structure, and a more trimodal eddy-driven jet, relative to CMIP5, but it still struggles with under-persistent regimes and too little European blocking when compared to reanalysis. Reduced regime persistence can be understood, at least in part, as a result of jets that are too fast and eddy feedbacks on the jet stream that are too weak – structural errors that do not noticeably improve in higher-resolution models. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates - internally consistently - the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process. [ABSTRACT FROM AUTHOR]

This paper focuses on early Holocene rapid climate change (RCC) records in the Mediterranean zone, which are under-represented in continental archives (9.2 to 8.2 ka events) and on their impact on prehistoric societies. This lack of data handicaps the general interpretation of climate impacts on human societies, which flourished in recent years. Key questions remain about the impact of early Holocene cooling events on the Mediterranean climate, ecosystems and human societies. In this paper, we discuss some examples from river and lake systems from the eastern to central Mediterranean area (central Anatolia, Cyprus, northeastern and northwestern Greece) that illustrate some palaeohydrological and erosion variations that modified the sustainability of the first Neolithic populations in this region. Results allow us to present direct land-sea correlations and to reconstruct regional long-term trends as well as millennial- to centennial-scale climatic changes. In this context, we question the socio-economic and geographical adaptation capacities of these societies (mobility, technology, economic practices, social organisation) during the "early Holocene" interval (11.7 to 8.2 ka), which corresponds partly to the Sapropel 1 deposition in the eastern Mediterranean sea. [ABSTRACT FROM AUTHOR]

The three IASI instruments on-board the Metop family of satellites have been sounding the atmospheric composition since 2006. More than 30 atmospheric gases can be measured from the IASI radiance spectra, allowing the improvement of weather forecasting, and the monitoring of atmospheric chemistry and climate variables. The early detection of extreme events such as fires, pollution episodes, volcanic eruptions, or industrial releases is key to take safety measures to protect inhabitants and the environment in the impacted areas. With its near real time observations and good horizontal coverage, IASI can contribute to the series of monitoring systems for the systematic and continuous detection of exceptional atmospheric events, in order to support operational decisions. In this paper, we describe a new approach for the near real time detection and characterization of unexpected events, which relies on the principal component analysis (PCA) of IASI radiance spectra. By analysing both the IASI raw and compressed spectra, we applied a PCA-granule based method on various past well documented extreme events such as volcanic eruptions, fires, anthropogenic pollutions and industrial accidents. We demonstrate that the method is well suited to detect spectral signatures for reactive and weak absorbing gases, even for sporadic events. Long-term records are also generated for fire and volcanic events, by analysing the available IASI/Metop-B data record. [ABSTRACT FROM AUTHOR]

Water productivity (or efficiency) data informs water policy, zoning and planning along with water allocation decisions under water scarcity pressure. This paper demonstrates that different water productivity metrics lead to different conclusions about who is using water more effectively. In addition to supporting the population's drinking and sanitation needs, water generates many other public and private social, environmental, and economic values. For the group of municipalities comprising the Phoenix Metropolitan Area we compare several water productivity metrics by calculating the Water Value Intensity (WVI) of potable water delivered by the municipality to its residential and non-residential customers. Core cities with more industrial water uses are less productive by the conventional efficiency measure of water used per capita, but core cities generate more tax revenues, business revenues, and payroll revenues per unit of water delivered, achieving a higher water productivity by these measures. We argue that policymakers should consider a more diverse set of socio-economic water productivity measures to ensure that a broader set of values are represented in water allocation policies. [ABSTRACT FROM AUTHOR]

For the last 25 years, CO-PDD (Cézeaux-Aulnat-Opme-puy de Dôme) has evolved to become a full instrumented platform for atmospheric research. It has received credentials as a national observing platform in France and is internationally recognized as a global station in the GAW (Global Atmosphere Watch) network. It is a reference site of European and national research infrastructures ACTRIS (Aerosol Cloud and Trace gases Research Infrastructure) and ICOS (Integrated Carbon Observing System). The site located on top of the puy de Dôme mountain (1465 m a.s.l.) is completed by additional sites located at lower altitudes and adding the vertical dimension to the atmospheric observations: Opme (660 m a.s.l.), Cézeaux (410 m), and Aulnat (330 m). The integration of different sites offers a unique combination of in situ and remote sensing measurements capturing and documenting the variability of particulate and gaseous atmospheric composition, but also the optical, biochemical, and physical properties of aerosol particles, clouds, and precipitations. Given its location far away from any major emission sources, its altitude, and the mountain orography, the puy de Dôme station is ideally located to sample different air masses in the boundary layer or in the free troposphere depending on time of day and seasons. It is also an ideal place to study cloud properties with frequent presence of clouds at the top in fall and winter. As a result of the natural conditions prevailing at the site and of the very exhaustive instrumental deployment, scientific studies at the puy de Dôme strongly contribute to improving knowledge in atmospheric sciences, including the characterization of trends and variability, the understanding of complex and interconnected processes (microphysical, chemical, biological, chemical and dynamical), and the provision of reference information for climate/chemistry models. In this context, CO-PDD is a pilot site to conduct instrumental development inside its wind tunnel for testing liquid and ice cloud probes in natural conditions, or in situ systems to collect aerosol and cloud. This paper reviews 25 years (1995–2020) of atmospheric observation at the station and related scientific research contributing to atmospheric and climate science. [ABSTRACT FROM AUTHOR]

The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and WorldClim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the root-mean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments. [ABSTRACT FROM AUTHOR]

This paper asserts that three principal frameworks for climate change adaptation can be recognised in the literature: scenario-led (SL), vulnerability-led (VL) and decisioncentric (DC) frameworks. A criterion is developed to differentiate these frameworks in recent adaptation projects. The criterion features six key hallmarks as follows: (1) use of climate model information; (2) analysis of metrics/units; (3) socio-economic knowledge; (4) stakeholder engagement; (5) adaptation of implementation mechanisms; (6) tier of adaptation implementation. The paper then tests the validity of this approach using adaptation projects on the Suffolk coast, UK. Fourteen adaptation plans were identified in an online survey. They were analysed in relation to the hallmarks outlined above and assigned to an adaptation framework. The results show that while some adaptation plans are primarily SL, VL or DC, the majority are hybrid, showing a mixture of DC/VL and DC/SL characteristics. Interestingly, the SL/VL combination is not observed, perhaps because the DC framework is intermediate and attempts to overcome weaknesses of both SL and VL approaches. The majority (57 %) of adaptation projects generated a risk assessment or advice notes. Further development of this type of framework analysis would allow better guidance on approaches for organisations when implementing climate change adaptation initiatives, and other similar proactive long-term planning. [ABSTRACT FROM AUTHOR]

This study investigates the validity of wine must quality as an April-to-August temperature proxy between 1420 and 2019 based on expert ratings and quality measurements from Germany, Luxembourg, eastern France, and the Swiss Plateau. This is highly relevant as uncertainties remain on past climate variations during this period. The evidence was reviewed according to the best practice of historical climatology. Expert ratings tended to agree with Oechsle density measurements that gradually replaced them from the 1840s. A statistical model calibrated to predict wine must quality from climate data explains 75 % of the variance, underlining the potential value of wine must quality as a climate proxy. Premium crops were collected in years of early harvest involving high insolation during maturation, while poor crops resulted from very late harvests in cold and wet summers. An analysis of daily weather types for high- and low-quality years after 1763 shows marked differences. On a decadal timescale, the average quality was highest from 1470 to 1479, from 1536 to 1545, and from 1945 to 1954. Poor crops were collected in periods with prevailing cold and wet summers such as 1453 to 1466, 1485 to 1494, 1585 to 1614, 1685 to 1703, 1812 to 1821, and 1876 to 1936. In the period of enhanced warming after 1990, high quality became the rule. [ABSTRACT FROM AUTHOR]

Small-scale floods are a consequence of high precipitation rates in small areas that can occur along frontal activity and convective storms. This situation is expected to become more severe due to a warming climate, when single precipitation events resulting from deep convection become more intense (super Clausius-Clapeyron effect). Regional climate model (RCM) evaluations and inter-comparisons have shown that there is evidence that an increase in RCM resolution and, in particular, at the convection-permitting scale will lead to a better representation of the spatial and temporal characteristics of heavy precipitation at small and medium scales. In this paper, the benefits of grid size reduction and bias correction in climate models are evaluated in their ability to properly represent flood generation in smalland medium-sized catchments. The climate models are sequentially coupled with a distributed hydrological model. The study area is the Eastern Alps, where small-scale storms often occur along with heterogeneous rainfall distributions leading to a very local flash flood generation. The work is carried out in a small multi-model framework using two different RCMs (CCLM and WRF) in different grid sizes. Bias correction is performed by the use of the novel scaled distribution mapping (SDM), which is similar to the usual quantile mapping (QM) method. The results show that, in the investigated RCM ensemble, no clear added value of the usage of convection-permitting RCMs for the purpose of flood modelling can be found. This is based on the fact that flood events are the consequence of an interplay between the total precipitation amount per event and the temporal distribution of rainfall intensities on a sub-daily scale. The RCM ensemble is lacking in one and/or the other. In the small catchment (< 100 km²), a favourable superposition of the errors leads to seemingly good CCLM 3 km results both for flood statistics and seasonal occurrence. This is, however, not systematic across the catchments. The applied bias correction only corrects total event rainfall amounts in an attempt to reduce systematic errors on a seasonal basis. It does not account for errors in the temporal dynamics and deteriorates the results in the small catchment. Therefore, it cannot be recommended for flood modelling. [ABSTRACT FROM AUTHOR]

The article presents comments on the article "Joint Effect of the Western and Eastern Pacific Warm Pools on El Nino-Southern Oscillation (ENSO) Cycle." It notes that the authors have used a very long datasets and have made a number of points about the changes in climatology over the period, however, the sources of data failed to discuss the changes on temperatures. It also observes that the data has assumed the value of the expansion and contraction of the warm pools and has confused the correlation between the cause and effect.

Particle depolarization ratio retrieved from lidar measurements are commonly used for aerosol-typing studies, microphysical inversion, or mass concentration retrievals. The particle depolarization ratio is one of the primary parameters that can differentiate several major aerosol components but only if the measurements are accurate enough. The accuracy related to the retrieval of particle depolarization ratios is the driving factor for assessing and improving the uncertainties of the depolarization products. This paper presents different depolarization calibration procedures used to improve the quality of the depolarization data. The results illustrate a significant improvement of the depolarization lidar products for all the selected lidar stations that have implemented depolarization calibration procedures. The calibrated volume and particle depolarization profiles at 532 nm show values that fall within a range that is generally accepted in the literature. [ABSTRACT FROM AUTHOR]

During the World Climate Conference-3, Capacity Development has been acknowledged as a transversal component underpinning all the other Pillars of the Global Framework for Climate Services. Within the Mediterranean basin, the interest of climate services based on seasonal climate forecasts is rising because they provide an opportunity for developing a proactive approach towards water management. In 2014, the Regional Training Center (RTC) in Italy, in agreement with World Meteorological Organization (WMO) and member countries of Region I and VI, identified seasonal climate forecasts as a strategic subject of capacity development for the Mediterranean Region. Following design-based research methods, this paper presents the evolution of the training approaches adopted, from classroom lessons to a blend of practical and theoretical classroom and distance learning. This evolution, as well as the rising satisfaction of trainees' expectations encouraged WMO and the RTC to widen the spectrum of beneficiaries and to make the resulting course materials available for other regions and RTCs as a course package. The course package provides essential guidelines to facilitate adoption and adaptation of the course by different institutions and instructors, including those in other WMO Regions, based on regional or institutional learning needs and standards, while also serving the needs of individual learners. [ABSTRACT FROM AUTHOR]

Reanalysis data sets are widely used to understand atmospheric processes and past variability, and are often used to stand in as "observations" for comparisons with climate model output. Because of the central role of water vapor (WV) and ozone (O3) in climate change, it is important to understand how accurately and consistently these species are represented in existing global reanalyses. In this paper, we present the results of WV and O3 intercomparisons that have been performed as part of the SPARC (Stratosphere- troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). The comparisons cover a range of timescales and evaluate both inter-reanalysis and observation-reanalysis differences. We also provide a systematic documentation of the treatment of WV and O3 in current reanalyses to aid future research and guide the interpretation of differences amongst reanalysis fields. The assimilation of total column ozone (TCO) observations in newer reanalyses results in realistic representations of TCO in reanalyses except when data coverage is lacking, such as during polar night. The vertical distribution of ozone is also relatively well represented in the stratosphere in reanalyses, particularly given the relatively weak constraints on ozone vertical structure provided by most assimilated observations and the simplistic representations of ozone photochemical processes in most of the reanalysis forecast models. However, significant biases in the vertical distribution of ozone are found in the upper troposphere and lower stratosphere in all reanalyses. In contrast to O3, reanalysis estimates of stratospheric WV are not directly constrained by assimilated data. Observations of atmospheric humidity are typically used only in the troposphere, below a specified vertical level at or near the tropopause. The fidelity of reanalysis stratospheric WV products is therefore mainly dependent on the reanalyses' representation of the physical drivers that influence stratospheric WV, such as temperatures in the tropical tropopause layer, methane oxidation, and the stratospheric overturning circulation. The lack of assimilated observations and known deficiencies in the representation of stratospheric transport in reanalyses result in much poorer agreement amongst observational and reanalysis estimates of stratospheric WV. Hence, stratospheric WV products from the current generation of reanalyses should generally not be used in scientific studies. [ABSTRACT FROM AUTHOR]

Survey after survey from across the globe suggest that climate education is floundering, despite climate education being embedded in international treaties to address the climate crisis (the UNFCCC and subsequent Paris Agreement) and the latest scientific assessment reports (IPCC) stressing the importance of climate education. The IPCC also acknowledges forces hostile to climate education, namely climate denialism sponsored by the energy-industrial complex. The latter fought the science of climate change and climate education by unleashing one of the greatest propaganda campaigns in history using the denial machine. Climate change is studied by the physical sciences, but climate denial is the purview of the social sciences; the latter have revealed the why and how of climate denialism and the inner workings of the denial machine. A major psychological factor is individual fear among conservatives that climate change legislation represents a threat to their values and identity, and to protect their ideology they turned to climate denialism, also known as the "climate change countermovement" by sociologists. Climate-denial organizations, supported by the energy-industrial complex, are interfering with the teaching of the science of climate change to our children. A purpose of this review is to draw attention to the growing threat of climate denialism to climate education, supported by specific examples of the influence of the energy-industrial complex in primary and secondary school classrooms. [ABSTRACT FROM AUTHOR]

The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts. [ABSTRACT FROM AUTHOR]

In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice. [ABSTRACT FROM AUTHOR]

Comparative analysis has been a little used approach to the teaching of hydrology. Instead, hydrology is often taught by introducing fundamental principles with the assumption that they are sufficiently universal to apply across most any hydrologic system. In this paper, we illustrate the value of using comparative analysis to enhance students" insights into the degree and predictability of future nonstationarity in flood frequency analysis. Traditionally, flood frequency analysis is taught from a statistical perspective that can offer limited means of understanding the nature of non-stationarity. By visually comparing graphics of mean daily flows and annual peak discharges (plotted against Julian day) for watersheds in a variety of locales, distinct differences in the timing and nature of flooding in different regions of the US becomes readily apparent. Such differences highlight the dominant hydroclimatological drivers of different watersheds. When linked with information on the predictability of hydroclimatic drivers (hurricanes, atmospheric rivers, snowpack melt, convective events) in a changing climate, such comparative analysis provides students with an improved physical understanding of flood processes and a stronger foundation on which to make judgments about how to modify statistical techniques for making predictions in a changing climate. We envision that such comparative analysis could be incorporated into a number of other traditional hydrologic topics. [ABSTRACT FROM AUTHOR]

Although storminess is often cited as a driver of long-term coastal erosion, a lack of suitable datasets has only allowed objective assessment of this claim in a handful of case studies. This reduces our ability to understand and predict how the coastline may respond to an increase in "storminess" as suggested by global and regional climate models. With focus on 16 km of the Sefton coastline bordering the eastern Irish Sea (UK), this paper analyses available measured datasets of water level, surge level, wave height, wind speed and barometric pressure with the objective of finding trends in metocean climate that are consistent with predictions. The paper then examines rates of change in shoreline position over the period 1894 to 2005 with the aim of establishing relationships with climatic variability using a range of measured and modelled metocean parameters (with time spans varying from two to eight decades). With the exception of the mean monthly wind speed, available metocean data do not indicate any statistically significant changes outside seasonal and decadal cycles. No clear relationship was found between changes in metocean conditions and rates of shoreline change along the Sefton coast. High interannual variability and the lack of long-term measurements make unambiguous correlations between climate change and shoreline evolution problematic. However, comparison between the North Atlantic Oscillation winter index (NAOw) and coastline changes suggest increased erosion at times of decreasing NAOw values and reduced erosion at times of increasing NAOw values. Erosion tends to be more pronounced when decreasing NAOw values lead to a strong negative NAO phase. At present, anthropogenic changes in the local sediment budget and the short-term impact of extreme events are still the largest threat likely to affect coastal flooding and erosion risk in the short- and medium-term. Nevertheless, the potential impacts of climate change in the long-term should not be ignored. [ABSTRACT FROM AUTHOR]

In January/February 2006, heavy snowfalls in Bavaria (Germany) lead to a series of infrastructural damage of catastrophic nature. Since on many collapsed roofs the total snow load was not exceptional, serious engineering deficiencies in roof construction and a sudden rise in the total snow load were considered to be the trigger of the events. An analysis of the then meteorological conditions reveals, that the early winter of 2005/2006 was characterised by an exceptional continuous snow cover, temperatures remained around the freezing point and no significant snowmelt was evident. The frequent freezing/thawing cycles were followed by a general compaction of the snow load. This resulted in a re-distribution and a new concentration of the snow load on specific locations on roofs. With respect to climate change, the question arises as to whether the risks relating to snow loads will increase. The future probability of a continuous snow cover occurrence with frequent freezing/thawing cycles will probably decline due to predicted higher temperatures. However, where temperatures remain low, an increase in winter precipitation will result in increased snow loads. Furthermore, the variability of extremes is predicted to increase. If heavy snowfall events are more frequent, the risk of a trigger event will likely increase. Finally, an attempt will be made here in this paper to outline a concept for an operational warning system for the Bavarian region. This system envisages to predict the development and risk of critical snow loads for a 3-day time period, utilising a combination of climate and snow modelling data and using this together with a snow pillow devic (located on roofs) and the results of which. [ABSTRACT FROM AUTHOR]