CL3.1.2

In the subproject CoDEx of the BMBF climXtreme project , we are investigating different data compression techniques to detect and attribute changes in the frequency and severity of extreme weather events in a changing climate. Especially for local processes on the atmospheric mesoscale, climate change signals are often masked by additional variability, resulting in poor signal-to-noise ratios. Therefore, these only become visible when the data are analyzed in compressed form. Our focus is on unsupervised learning approaches such as principal component analysis developed specifically for extremes. We focus on extreme precipitation over Germany and analyze how different data compression techniques can be used in a detection and attribution (D&A)study. Besides others, we use the approach proposed by Cooley and Thibaud (2019) on the decomposition of the tail pairwise dependence matrix, as an analogue to the covariance matrix for extremal dependence. Furthermore, we use a dualtree wavelet transform to study changes in extreme precipitation at different scales and different orientations. A D&A study will provide deeper insight into the effects of climate change on extreme precipitation events. 

How to cite: Szemkus, S. and Friederichs, P.: Climate change detection and attribution in extreme precipitation using compact representations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8045, https://doi.org/10.5194/egusphere-egu22-8045, 2022.

Extreme precipitation, both the occurrence and intensity, over the Northeast United States has significantly increased since the 1990s, evidenced by observations. The most salient increase has happened in the fall season (September to November). Understanding the attribution and projection of long-term trends in regional extreme precipitation is essential to adaptationion planning such as infrastructure upgrade. However, such work is challenging due to uncertainties caused by internal climate variability and the requirement of medium-to-high model resolution as well as ensemble size. In this work, we leverage the newly-developed GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless System for Prediction and EArth System Research) models which generate 25-km high-resolution simulations (ten members; SPEAR-HI) and 50-km large-ensemble simulations (30 members; SPEAR-MED) for both historical simulations from 1921 to 2014 and projections for the Shared Socioeconomic Pathway 5-8.5 (SSP585) from 2014 to 2100. We aim to address two related scientific questions using GFDL-SPEAR: (1) what are the factors that have contributed to the increasing autumn extreme precipitation over the Northeast US since 1990s? How much of the increase could be attributed to anthropogenic forcing? (2) when would the increased extreme precipitation in response to forced climate change emerge from the noise of internal climate variability?

Our preliminary results first suggest that higher atmospheric resolution in climate models is critical to facilitate the simulations of regional extreme precipitation. For example, SPEAR-HI can simulate comparable frequency of extreme precipitation over the Northeast US (rain rate > 50 mm/day), compared to the observation; while SPEAR-MED underestimates the frequency. Second, the recent increasing Northeast US extreme precipitation is unlikely due to the warming North Atlantic sea surface temperature, even though the timing of the abrupt increase in extreme precipitation coincided with the timing when the Atlantic Multidecadal Oscillation shifted from a cold to warm phase in the mid-1990s. Our ongoing work focuses on evaluating the attributions from other factors including internal variability, aerosols, and greenhouse gas. Last, we analyze SPEAR-HI SSP585 projections and extended control simulations starting from the year 1850. We estimate that the anthropogenically forced increase in the Northeast US autumn extreme precipitation would emerge from the noise of internal climate variability around the 2040s. However, ongoing work will employ more systematic methods to estimate the time of emergence.

How to cite: Jong, B.-T. and Delworth, T. L.: Using an ensemble of high-resolution climate model simulations to detect, attribute, and project changes in extreme rainfall over the Northeast U.S., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13358, https://doi.org/10.5194/egusphere-egu22-13358, 2022.

Between 22-26 June 2009, Austria witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in southeast Styria, and property damages worth €10 Million in Styria itself. Elsewhere in Austria, flooding amounted to reparations worth €40 Million. Numerous synoptic-scale studies indicated the presence of a cut-off low over central Europe and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may manifest into a more intense event due to the higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation, more so in a topographically heterogeneous region such as the GAR (Greater Alpine Region).

The flooding and landslides caused in the region raise an alarm and thus motivate this study whereby we investigate if the rainfall event did become stronger with time due to climate change compared to how it would have been in a counterfactual (climate change free) past. Here we have deployed the CCLM high-resolution regional model coupled with a statistical landslide model to simulate this event (rainfall and landslides) in a pseudo (surrogate) warming scenario. A marked decrease in rainfall intensity is observed in the simulations for 1° cooler climate (pre-industrial past) and the consequent landslide risk is reduced varying across GCMs that were used to derive the boundary conditions from.

We discuss the results from the lens of attribution perspective - how conditional attribution is much more useful compared to the conventional risk-based approach of attributing extreme events. The novelty of our approach lies in using a high-resolution convection-permitting regional model for a landslide attribution study.

How to cite: Mishra, A. N., Maraun, D., Truhetz, H., Knevels, R., Bevacqua, E., Proske, H., Petschko, H., Leopold, P., and Brenning, A.: Attribution of 2009 extreme rainfall & landslide event in Austria, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5845, https://doi.org/10.5194/egusphere-egu22-5845, 2022.

Statistically rigorous methods to attribute large-scale, long-term changes in observed climate to anthropogenic forcing are well established and the attribution of individual extreme events has advanced rapidly too. Further attributing climate impacts in society and ecosystems can be important for understanding and assessing loss and damage, for informing adaptation policies, and for motivating both adaptation and mitigation efforts. For this purpose, a counterfactual climate dataset was recently published within the Inter-Sectoral Impact Model Intercomparison Project (Mengel et al., 2021). Constructed by removing the long-term shifts in daily reanalysis data that are correlated to global-mean temperature change, the dataset does not address anthropogenic climate change, but its large spatial and temporal coverage and the range of variables covered as well as minimum requirements on computational tools and data make it a desirable resource for this interdisciplinary problem.

Here, we trial the use of that counterfactual dataset for the quantification of climate impacts on agricultural crop yields, which are of paramount importance to many of the regions most exposed and vulnerable to climate change, not least for food security. We present results from case studies that examine the impacts of selected drought events and are chosen based on reports of substantial food security impacts, on the availability of crop yield data, and on the existence of published scientific literature of a corresponding climate attribution study. The latter allows the comparison with results using methods that isolate the anthropogenic (combined, or by individual forcing) climate change signal. Together with more systematic discussion, this gives an idea of the degree to which our results on the contribution of any climate variations to the observed impacts may be a proxy for the anthropogenic climate change contribution specifically.

Impacts are explicitly simulated using statistical crop models that are established in the agricultural and agronomic literature, built and validated based on the observed record. The use of the single-realisation, weather-preserving factual and counterfactual dataset gives a deterministic rather than probabilistic estimate, but parametric and structural crop-model uncertainty is characterised, and the robustness of the results to different observational crop-yield datasets assessed. We collaborate with local stakeholders to ensure appropriate consideration of non-climatic factors and to improve data availability and quality. Our work combines perspectives of climate attribution, disaster risk reduction, and agricultural science to enhance attributing loss and damage in agriculture to climate change.

How to cite: Undorf, S., Schauberger, B., and Gornott, C.: Attributing observed climate change impacts in agriculture using observationally-derived counterfactual climate data and statistical crop-yield modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4326, https://doi.org/10.5194/egusphere-egu22-4326, 2022.

Probabilistic event attribution aims to quantify the role of anthropogenic climate change in altering the intensity or probability of extreme climate and weather events. It was originally conceived to calculate the costs associated with any increased likelihood of the meteorological event in question. However, only recently have such studies attempted to divide liability between polluting nations and ascribe a cost. Recent protests indicate a perception that older generations have the greater responsibility for climate change. In this paper, we examine how a portion of the cost of an event can be attributed to any individual person, according to their age and nationality. We demonstrate that this is quantitatively feasible using the example of the 2018 summer heatwave in eastern China and its impact on aquaculture. A relatively simple technique finds sample individuals responsible for between 0.53 and 18.10 yuan, increasing with their age and their country’s emissions over their adult lifetime since the first international consensus on carbon emissions was reached. This provides an illustration of the scale of such responsibilities, and how it is affected by national development and demographics. Such data can support decisions, at national and international levels, on how to fund recovery from climate impacts. It offers a simple quantitative approach for individuals to know their impact on the climate, or for governments to use in making policy decisions about how best to distribute costs of climate change.

How to cite: Lott, F., Ciavarella, A., Kennedy, J., King, A., Stott, P., Tett, S., and Wang, D.: Quantifying the contribution of an individual to making extreme weather events more likely, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-705, https://doi.org/10.5194/egusphere-egu22-705, 2022.

Although anthropogenic global warming is well-known within the scientific community, the public is still not certain how to associate specific local weather and climate events to this issue. Therefore, it is essential to raise public awareness by providing sound and readily understood scientific information, and to explain how humans contribute to specific (extreme) events. Although a few case studies for past high-impact weather events were assessed in Hungary, a systematic analysis of long-term past and future trends of these indices is missing, and their linkage to anthropogenic activity has not been addressed at all. Our attribution project (started in September 2021) aims to fill this gap in Hungary: the results of the analysis of seasonally relevant indices are published in each season at the time of an (extreme) event occurrence. The dissemination is done via an already established Hungarian platform (https://masfelfok.hu/) reaching the public with readily understood climate change information through their broad media coverage and a large social media network.

The assessments are prepared within the project using several data sources: (1) an ensemble of CMIP6 global climate model simulations of both natural-only forcings and historical runs, (2) an ensemble of regional climate model simulations from Euro-CORDEX, including both RCP4.5 and RCP8.5 scenarios, (3) a fine-resolution, homogenized observation-based gridded data for Hungary, (4) the ERA5 reanalysis. We address seasonally relevant extreme and compound events, and the attribution of pre-selected indices to anthropogenic activity through their intensity, duration, and frequency changes. For instance, frost days, annual temperature minima and return values, snowfall and heavy snowfall days are evaluated for winter, while the start of vegetation period, late frost, dry and heavy precipitation days for spring, as they are of most public interest. Furthermore, we determine how many seasonal record low/record high breakings happen and how large area within the domain is affected.

Preliminary winter results suggest that the decrease of frost days in Hungary is clearly due to anthropogenic activity, while it is not the case for the annual minimum temperatures but will be in the future. No significant decrease has been detected for the snowfall and heavy snowfall days, and the effects of anthropogenic activity on these indices will only occur following the pessimistic future scenario.

How to cite: Szabó, P., Bartholy, J., Barna, Z., Bokros, K., Bordi, S., Mráz, A., Pieczka, I., and Pongrácz, R.: Attribution of seasonally relevant winter and spring climate indices in Hungary, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9707, https://doi.org/10.5194/egusphere-egu22-9707, 2022.

Due to their relevance for regional weather and climate, teleconnections are an extremely active area of research. One key task is to quantify the contribution of a teleconnection to regional anomalies in both models and observations. This is, for instance, important to improve forecasts on time scales ranging from subseasonal to multidecadal, or to attribute ensemble spreads to changes in large-scale drivers. However, robustly estimating the effects of a teleconnection remains challenging due to the often simultaneous influences of multiple climate modes. While physical knowledge about the involved mechanisms is often available, how to extract a particular causal pathway from data are usually unclear.

In this talk I argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. I illustrate some of the key concepts of this theory with simple examples of well-known atmospheric teleconnections. Moreover, I show how the deductive nature of a causal approach can help to assess the plausible influence of Arctic sea ice loss on mid-latitude winter weather, thereby helping to reconcile differences between models and observations. I finally discuss the particular challenges and advantages a causal inference-based approach implies for climate science.

References

Kretschmer, M., Adams, S. V., Arribas, A., Prudden, R., Robinson, N., Saggioro, E., & Shepherd, T. G. (2021). Quantifying Causal Pathways of Teleconnections, Bulletin of the American Meteorological Society, 102(12), E2247-E2263. Retrieved Jan 13, 2022, from https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml

Kretschmer, M., Zappa, G., and Shepherd, T. G. (2020), The role of Barents–Kara sea ice loss in projected polar vortex changes, Weather and Climate Dynamics, doi: 10.5194/wcd-1-715-2020

How to cite: Kretschmer, M.: Quantifying Causal Pathways of Teleconnections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8246, https://doi.org/10.5194/egusphere-egu22-8246, 2022.

The latest generation of climate models (CMIP6) have very different large-scale surface temperature variability and inconsistencies with observed climate have been found in the variability in several regions. Given that detection and attribution, in common with many climate analyses, relies on model internal variability for uncertainty ranges, it is crucial to better constrain this variability. Here, we compare the latest climate models to observed variability to determine where and on what timescales discrepancies occur, with the models found to be, in general, too variable on annual timescales over land and with not as much variability as the observations particularly over the Southern oceans at multi-decadal timescale. We further use paleo-proxy reconstructions, supported by observational datasets finding that the majority of models have variability consistent with large-scale mean temperature on multi-annual and multi-decadal timescales. Finally, the presentation will explore the implications of these findings on key detection and attribution analyses, in particular the attribution of warming since pre-industrial times to anthropogenic forcings.

How to cite: Schurer, A., Luecke, L., and Hegerl, G.: Constraining internal surface temperature variability and its implications for detection and attribution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5193, https://doi.org/10.5194/egusphere-egu22-5193, 2022.

The North Atlantic Oscillation (NAO) is the leading mode of climate variability over the North Atlantic region, affecting temperature and rainfall over timescales from days through to seasons and decades. Previous studies have shown that variations in the NAO yield significant multi-decadal trends in European rainfall, especially in winter, and the magnitude of past multi-decadal NAO variability is generally not reproduced by CMIP class models This has important implications for deriving observational constraints, and the application of these scaling factors to projections of future European rainfall.

We have constructed two sets of multi-model-mean spatiotemporal fingerprints of European rainfall: one set that retains NAO variability, and another set that excludes the variability associated with the NAO (removing it using a simple regression technique). Following the so-called Allen-Scott-Kettleborough ‘ASK’ method, we conduct total-least-squares regressions using the two different sets of fingerprints against the observations in order to analyse the impact of removing the NAO in potentially enhancing the signal-to-noise. The derived scaling factors shed light on the ability of CMIP6 models to reproduce the magnitude of the forced response in precipitation, and confidence intervals for each of the scaling factors describes the range of magnitudes of the model response that are consistent with the observed signal.

Here we focus on one clear example, northern European rainfall, although we have also analysed additional European regions and surface air temperature across all of the seasons. There is an increasing trend in observed rainfall anomalies associated with the NAO over northern Europe, most pronounced in winter, which is not replicated in models. Once the variability associated with the NAO is removed the magnitude of the observed trends is reduced, and the scaling factors (derived in the ASK framework) are similarly reduced.

Along with a shift in the magnitude that comes from the modified observations, the constraint also tightens (the spread in consistent scaling factors narrows) due to the increased signal-to-noise in the modelled response once the NAO is removed from the simulations. This has important implications for the use of scaling factors to constrain future projections of European climate. The observed decadal to multi-decadal trends resulting from known modes of internal variability should be accounted for in the derivation of scaling factors to better capture the forced signal and bolster confidence in the constrained projections.

How to cite: Ballinger, A., Schurer, A., and Hegerl, G.: Accounting for the NAO when applying observational constraints to future European climate projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12074, https://doi.org/10.5194/egusphere-egu22-12074, 2022.

Monitoring trends and continuous changes to complement local studies on discharge and precipitation are becoming increasingly important, assessing systematically its temporal and spatial variation characteristics. It is also necessary to provide detailed study on the driving mechanisms of those variations, which can be a result of anthropogenic activities as it specifically affects particular regions and terrains. These data play very significant roles in measuring and forecasting potential impacts and lead to future improved regional flow regulations for better water resources management. One of the most effective methods for observing the effects of climate change on hydrometeorological variables is the trend analysis, with recently new graphical methods that represents an innovative alternative to the classical ones. In this work, mean monthly, annual mean, minimum and maximum precipitation was examined to analyze spatiotemporal variations, seasonality shifts and trends with records that can extend from 1910 to 2019 in the hydrometeorological network. The flow discharge were also analysed considering catchments with unimpaired streamflow in unregulated rivers. Different homogeneity and shift detection methods were used to check their homogeneity before conducting trend analysis. Innovative Polygon Trend Analysis (IPTA), Innovative trend analysis (ITA) with the Significance Test and Mann-Kendall (MK) non-parametric methods were compared, showing their sensitivity and their ability to explain a monthly trend sequence and periodicity in the studied region in order to explain adequately the temporal internal variability.

How to cite: Montenegro Gambini, J. I. and Cusipuma Ayuque, M.: Graphical analysis applications to study variability and trends of rainfall and streamflow in Colombian catchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13379, https://doi.org/10.5194/egusphere-egu22-13379, 2022.

Detection and attribution (D&A) of anthropogenically forced changes to precipitation is challenging due to the high internal variability of precipitation and the limited spatial and temporal coverage of the observational records. These factors result in a low signal-to-noise ratio of potential regional and even global trends.

Here, we use a statistical method – regularised linear regression, or ridge regression – to create physically interpretable detection metrics (fingerprints) for D&A of changes in the precipitation distribution with a high signal-to-noise ratio. The regression coefficients that make up the fingerprints of forced change are based on the CMIP6 multi-model archive data masked to match observational coverage, and are then applied to gridded precipitation observations to assess the degree of forced change detectable in the real-world climate. 

We show that the signature of forced change is detected and attributed to external forcing in two different observational datasets in global metrics of mean and extreme precipitation (PRCPTOT, and Rx1d, respectively). If the global mean trend is removed from the data, forced changes are still detected, indicating that climate change affects the spatial patterns of precipitation, and increasing confidence in the results of this method for D&A of precipitation, as well as in climate models capturing the relevant processes that contribute to the regional patterns of change. Furthermore, we show the sensitivity of our D&A results to several ‘design choices’, including target metric of forced change, regularisation parameter, season of interest, (spatial coverage of) observational dataset used, the forced trend length and the region of interest (tropics vs. subtropics). 

The method is largely insensitive to target metric and regularisation parameter, increasing confidence in the robustness of the results. However, we find that June-July-August generally has low forced trend signal-to-noise ratio in both mean and extreme precipitation. Furthermore, the observational dataset choice affects detectability not only through coverage differences but also dataset disagreement, and the chosen trend length can result in different forced trends when comparing observations to model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed increase in extreme precipitation. Lastly, the detection models are found to rely primarily on the signal in extratropical northern hemisphere data, which is at least partly due to observational coverage, but potentially also due to presence of a more robust signal in the northern hemisphere in general. When these regions are excluded, detection of significant forced changes is no longer possible, which may also have implications for the ability to assess risks and inform adaptation policies in the tropics and the global south.

Ridge regression is powerful for D&A of the precipitation distribution, opening up possibilities for extension of the method to learn more about mechanisms driving forced changes in precipitation. However, internal variability, limited coverage in time and space, and dataset disagreement in precipitation data continue to play a large role.

How to cite: de Vries, I., Sippel, S., and Knutti, R.: Detection of forced changes in the precipitation distribution using ridge regression, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11239, https://doi.org/10.5194/egusphere-egu22-11239, 2022.

Anthropogenic aerosols (AER) have been found to impact both Earth’s energy and water cycle. Like greenhouse gases (GHG) they are an anthropogenic climate forcing, which will play an important role in shaping Earth’s future climate. To improve future predictions, it is, therefore, fundamental to understand and quantify the individual impacts these two forcings have on the climate system. This can be achieved by using detection and attribution methods facilitating the differentiation of the response of the climate system to different forcings.

Separating the signal of individual anthropogenic effects related to greenhouse gas and aerosol emissions is hindered by large uncertainties in the response to aerosol forcing in different climate models. Thus, in this study we investigate a joint change in temperature and precipitation to reduce the signal-to-noise ratio and better constrain the impact of anthropogenic aerosols since 1979. Building on previous findings on how aerosols affect climate, we focus on shifts in tropical precipitation by tracking wet/dry regions as well as changes in the interhemispheric temperature asymmetry (ITA) and the diurnal temperature range (DTR), due to its unique response to different radiative forcings. Individual fingerprints are derived from large-ensembles of historical single-forcing simulations from three models that are part of phase 6 of the Coupled Model Intercomparison Project (CMIP6).

We find inter-model agreement in the trends for wet regions, ITA, and DTR in single-forcing and historical (all-forcing) runs. Contrasting trends in these time series derived for AER-only and GHG-only simulations suggest that aerosols have offset some of the greenhouse gas induced precipitation and temperature changes in the past.  While a drying in the dry regions can be observed for GHG-only simulations, inter-model agreement is not found for aerosols. Early results show an improved constraint on the detection of a greenhouse gas signal when investigating a joint change in wet and dry regions, which is refined by including the other variables and indicators.

How to cite: Roesch, C., Ballinger, A., Schurer, A., and Hegerl, G.: Using temperature and precipitation combined to detect and attribute aerosol effects on large-scale climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8088, https://doi.org/10.5194/egusphere-egu22-8088, 2022.

Estimating when anthropogenically-forced signals emerge from ambient natural climate variability is crucial for climate-change detection. Here we propose a new method for estimating the emergence time of climate signals based on a significance test on the nonlinear trend rather than the commonly-used method based on a signal-to-noise ratio. This method is less sensitive to the choice of a confidence level (0.1, 0.05 or 0.01) than the previous method to commonly-used signal to noise ratios (0.5, 1 or 2). For signals that tend to emerge in the late 21^st century, our method tends to yield earlier detection dates, by taking the large number of degrees of freedom into account. Here, we apply this method to relative SST (RSST, SST minus its tropical mean) changes, which tend to emerge much later than SST change signals. RSST is an indicator of changes in the atmosphere vertical stability and thus of changes in tropical cyclones intensity and precipitation. By 2100, CMIP5/6 projections indicate greater than tropical average warming (positive RSST signal) in the central and eastern equatorial Pacific, equatorial Atlantic, and Arabian Sea, and reduced warming (negative RSST signal) in the three southern hemisphere subtropical gyres. In general agreement with observations, relative warming in the Arabian Sea and relative cooling in the South-Eastern Pacific are already detectable in a majority of models (median emergence time < 2020), making these regions suitable for testing a model's ability to predict a regional SST trend. In contrast, RSST signals in other regions do not become detectable until after 2050. Tropical precipitation projections indicate more (less) rainfall in regions of positive (negative) RSST change that typically emerge one or two decades later than RSST signals. This lack of currently-detectable regional rainfall trends in CMIP models makes it difficult to evaluate their ability to predict tropical regional rainfall trends. In general, signals tend to emerge later in CMIP6 than in CMIP5 due to both weaker signals and larger climate noise. The only exception is Sahel, where CMIP6 models already display a detectable rainfall increase, that is not yet detectable in CMIP5.

How to cite: Suresh, G., Suresh, I., Lengaigne, M., Vialard, J., Izumo, T., and Kwatra, S.: When do regional tropical climate signals become detectable in CMIP5/6 simulations?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8622, https://doi.org/10.5194/egusphere-egu22-8622, 2022.

In addition to their changes to carbon pools, land use and land cover changes (LULCC) alter climates biogeophysically through their effects on surface fluxes for energy and water. These resulting climatic changes in temperature manifest differently across study type (observational or model-based) and spatial-temporal scales depending on the predominance of albedo, evaporative fraction and surface roughness as causal factors. With growing future demand for land-exhaustive activities to address societal needs and interest in mitigation strategies involving reforestation/afforestation, it is important to understand how past LULCC contributed to climate change. Here we assess the prevalence of the historical LULCC signal in the warmest average month of daily maximum temperatures using regularized optimal fingerprinting for detection and attribution. We use the simulations of four global climate models from CMIP6 historical and hist-noLu experiments and separate observations from the Climatic Research Unit and Berkley Earth. Aggregating data according to the new IPCC AR6 reference regions and regressing observations onto hist-noLu and lu (historical - hist-noLu) in a 2-way regression, we find that LULCC is not sufficiently detectable at continental and global scales for four GCMs and their multi-model mean. This is confirmed by the nearly unchanging detectability of historical climate change in separate 1-way regressions for historical and hist-noLu. To further explore lu, we confirm the predominance of noise and the non-local effects of LULCC over its local effects by finding insignificant signal-to-noise ratios for the fingerprint of forest cover on lu using a principal component analysis.

How to cite: Grant, L.: Biogeophysical effects of land use and land cover change not detectable in CMIP6 warmest month., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2345, https://doi.org/10.5194/egusphere-egu22-2345, 2022.

Vegetation in Northeast China (NEC) has faced dual challenges posed by climate change and human activities. However, the factors dominating vegetation development and their contribution remain unclear and cannot be precisely discussed. In this study, we conducted a comprehensive evaluation of the response of vegetation in different land cover types, climate regions, and time scales to water availability from 1990 to 2018 based on the relationship between normalized difference vegetation index (NDVI) and Standardized Precipitation-Evapotranspiration Index (SPEI). The effects of human activities and climate change on vegetation development were quantitatively evaluated using the residual analysis method. We showed that the area percentage with a positive correlation between NDVI and SPEI increases with the extension of time scales. NDVI of grass, sparse vegetation, rain-fed crop, and built-up land as well as sub-humid and semi-arid areas (drylands) correlated positively with SPEI, and the correlations increased with the extension of time scales. The negatively correlated area was concentrated in humid areas or areas covered by forests and shrubs. The maximum water surplus period for irrigated crops and forests, shrubs, wetlands, humid areas were 1-month and 6-months, respectively. Vegetation water surplus in humid areas weakens with warming, and vegetation water constraints in drylands enhance. Moreover, potential evapotranspiration had an overall negative effect on vegetation and precipitation is a controlling factor for vegetation development in semi-arid areas. Within the period of study, 53% of the vegetated area in NEC showed a trend of improvement, which is mainly attributed to human activities (93%), especially through the implementation of ecological restoration projects in NEC. The relative role of human activities and climate change in vegetation degradation areas were 56% and 44%, respectively. Our findings highlight that the government should more explicitly consider the spatiotemporal heterogeneity of the influence of human activities and water availability on vegetation under changing climate, and improve the resilience of regional water resources. The relative proportions and roles map of climate change and human activities in vegetation change areas provide a basis for government to formulate local-based management policies.

How to cite: Xue, L., Kappas, M., Wyss, D., and Putzenlechner, B.: Assessment of climate change and human activities on vegetation development in Northeast China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7718, https://doi.org/10.5194/egusphere-egu22-7718, 2022.