There is overwhelming evidence that the impacts of climate change are already being observed in human and natural systems3. These effects are emerging in a range of different systems and at different scales, covering a broad range of research fields from glaciology to agricultural science, and marine biology to migration and conflict research1. The evidence base for observed climate impacts is expanding4, and the wider climate literature is growing exponentially5,6. Systematic reviews and systematic maps offer structured ways to collectively identify and describe this evidence while maintaining transparency, attempting to ensure comprehensiveness and reduce bias7. However, their scope is often confined to very specific questions covering no more than dozens to hundreds of studies.
In the climate science community, evidence-based assessments of observed climate change impacts are performed by the Intergovernmental Panel on Climate Change (IPCC)1. Since the first Assessment Report (AR) of the IPCC in 1990, we estimate that the number of studies relevant to observed climate impacts published per year has increased by more than two orders of magnitude (Fig. 1a). Since the third AR, published in 2001, the number has increased ten-fold. This exponential growth in peer-reviewed scientific publications on climate change5,6 is already pushing manual expert assessments to their limits. To address this issue, recent work has investigated ways to handle big literature in sustainability science by scaling systematic review and map methods to large bodies of published research using technological innovations and machine learning methods8–12.
Fully utilising the available knowledge on emerging climate change impacts is key to informing global policy processes13 as well as regional and local risk assessments and on-the-ground action on climate adaptation14,15. While the global policy process may be served well with literature assessments presenting results aggregated on the level of continents or world regions1,16, informing climate adaptation typically requires more highly localised and contextualised information on climate impacts17,18.
Another core challenge of literature reviews and assessments of observed climate impacts relates to the question of whether climate impacts can be attributed to anthropogenic forcing4. While anthropogenic climate change signals have been identified in observed trends in a number of variables4 including temperature19, precipitation20, sea level rise21, or water resources22, and selected extreme weather23 events, the confidence in these assessments is still subject to substantial regional variations and remains relatively tentative at smaller spatial scales even if very high confidence levels can be reached for larger scale (e.g., global scale) attribution findings. Confidence also strongly depends on the variable being considered, and specifically decreases further down the impact chain, i.e. for indicators of changes in human and natural systems that are driven by changes in other climate impact variables4. In addition, methodological approaches and robustness criteria for climate change attribution differ widely between studies and disciplines, requiring expert judgement on a case-by-case basis in order to compile a comprehensive evidence base.
This points towards the added value of joining the body of evidence documenting regional or local-scale studies about climate impacts linked to common climate drivers such as temperature and precipitation change to a spatially resolved detection/attribution database of those variables.
Using BERT, a state of the art deep learning language representation model24, we develop a machine learning pipeline to identify, locate and classify studies on observed climate impacts at a scale beyond that which is possible manually (see Extended Figure 1). We combine this spatially resolved dataset with an approach to attributing observed trends in surface temperature and precipitation at the grid cell level (5o x 5o and 2.5o x 2.5o cells respectively) to human influence on the climate. In doing so, we establish a new paradigm for assessing the impacts of climate change across human and natural systems.
Mapping over 100,000 impact studies
We searched two large bibliographic databases (Web of Science and Scopus) using an inclusive and transparent search method to systematically identify the literature on climate impacts. We assessed comprehensiveness by ensuring that our search string returned all references from tables 18.5-18.9 in AR5 WGII, which deal with the detection and attribution of climate impacts. Recent breakthroughs in natural language processing (NLP) have extended the capabilities of text classification. BERT (Bidirectional Encoder Representations from Transformers) is a deep learning language model trained using semi-supervised learning on massive corpora to represent text where word representations are dependent on context. The pretrained model can be fine-tuned on downstream tasks, and has achieved state of the art results across a range of NLP tasks. Using training data assembled by collaboratively screening and coding 2,629 abstracts, we use supervised machine learning, fine-tuning a DistilBERT model25, to classify, also based on the abstract text, documents relevant to understanding the observed impacts of climate change in general, and to predict the human or natural systems for which they document impacts (i.e., the impact categories), as well as the climate variable(s) driving the documented impacts. Uncertainty estimates for the predictions are derived from bootstrapping. We employ a nested cross-validation approach to hyperparameter tuning, model selection and classifier evaluation, and find that our binary inclusion classifier achieves an average F1 score of 0.71, and ROC AUC score of 0.92. The prediction of impact type is achieved with an average macro F1 score of 0.84 while the prediction of climate driver is achieved with an average F1 score of 0.79 (see Methods section and Extended Figures 1-5 for a detailed explanation of the labelling, machine learning approach and classifier performance).
Our query returned 603,759 unique documents (Fig.1a): many more than would have been possible to screen by hand. Of these we estimate that 100,724 (62,950-162,838) documents are relevant to understanding the observed impacts of climate change in general, based on the spread of inclusion/exclusion predictions obtained from our model via bootstrapping (Fig. 1a.). This base of relevant publications has grown substantially through the IPCC assessment cycles. 48,911 (39,602-79,464) articles have been published in the sixth assessment cycle so far; this represents more than twice the number of studies published during the AR5 period.
We used a geoparser pre-trained using neural networks26 to extract structured geographic information from the titles and abstracts of the studies in our database. Although the number of relevant studies in North America, Asia, and Europe is much higher than in South America, Africa, and Oceania, there is a large body of relevant studies available on all continents (fig 1.c). The relevant publications are also unevenly distributed across impact categories, with by far the largest number of studies 34,988 (18,520 - 65,666) documenting impacts on terrestrial and freshwater ecosystems (Fig 1.b.). However, the category with the comparably smallest coverage--mountains, snow and ice--still has 6,307 (3,526 - 12,228) studies.
In contrast to the map of observed impacts produced by the IPCC, we do not only include papers which formally attribute impacts to observed trends in climate. Instead, we take a more comprehensive approach reflecting that our objective is to map all possibly relevant studies on climate-related changes, rather than a list of studies where the relationship between an observed climate trend and specific impacts has been demonstrated with high confidence, or even linked to human influence on the climate. This includes studies attributing impacts to observed trends in climate variables, even where the authors do not attribute these trends to human influence, such, for example, a study documenting the influence of the date of snowmelt on the phenology and population growth of mammals27. In addition, we include studies which provide evidence on the sensitivity of human or natural systems to climate metrics, such as on how heart disease mortality responds to variations in temperature28. Finally, we include documents describing the impacts of extreme events and studies which detect significant trends in climate variables or climate extremes29, regardless of whether or not these trends are in line with the expected effects of anthropogenic climate change. We exclude all studies which only describe potential or modelled impacts of future climate change.
Combining geolocated literature with climate information
To add context on the role of anthropogenic climate change in driving impacts, or more precisely the role of historical changes in anthropogenic climate forcing agents such as greenhouse gases and aerosols, we combine our literature database of studies selected using machine learning with spatially explicit analysis of detectable and attributable trends in two key climate variables. Combining evidence from climate model simulations and observational datasets allows us to identify trends likely attributable in part to anthropogenic climate change for near-surface temperature and precipitation at the level of 5 degree (temperature) or 2.5 degree (precipitation) grid cells19,20. Here we apply this methodology to updated observational data until 2018 for temperature (Fig.2a) and until 2016 for precipitation (Fig.2b), analysing in each case trends from 1951. Grid cells in our categories +-2 or +-3 show where trends cannot be explained by internal variability and are either consistent with or greater than the expected change in climate model simulations that include anthropogenic forcing agents like greenhouse gas increases. We infer that these cells display detectable and at least partly attributable trends (see Methods for more details).
We next resolve the structured geographic information extracted from our studies, which range from continental scale down to individual watersheds or communities, to sets of grid cells (Extended Fig. 9, Methods). We can then derive the weighted number of studies per grid cell according to the number of grid cells to which each study relates. By combining studies related to temperature or precipitation with the gridded information on attributable trends in temperature and precipitation, this provides a necessary (though not necessarily sufficient) condition for a systematic two-step attribution to anthropogenic activities of the impacts predicted by the classifier30. Where studies documenting impacts associated with changes in temperature or precipitation co-occur with attributable trends in those variables, we claim that there is at least preliminary evidence for attributable impacts in these areas. This approach is similar in nature to the “joint attribution” applied in IPCC AR431,32.
In general, we note that this type of automated assessment procedure which we present here is no substitute for careful assessment by experts, but can identify large numbers of studies for a region that may point toward attributable human influence on impacts. Confidence in multi-step attribution claims depends on confidence in the attribution of the individual components (steps) along with the confidence or limitation in linking the different steps in the proposed causal chain32. One limitation of our partially automated two-step attribution approach is that we cannot verify that every temperature or precipitation trend cited in impact studies matches, either in sign, magnitude or time period, those attributed to human influence by the regional detection and attribution studies for temperature19 and precipitation20. This is a greater problem for studies driven by precipitation, where both wetting and drying trends occur with greater temporal variation, though these make up the minority of partially attributed studies and grid cells. We also note that not all studies in our database document impacts in response to trends in climate variables. Where impacts are attributed to extreme events or variation in temperature or precipitation, the fact that recent trends in temperature or precipitation can be attributed to human influence provides important context, but does not allow robust attribution of those impacts. These factors limit confidence in our cases of potential attribution of impacts to anthropogenic forcing. Our approach could be extended with more fine-grained analysis of studies or with attribution of additional signals in climate variables in order to make more robust attribution statements.
For 80% of global land area (excluding Antarctica), trends in temperature and/or precipitation can be attributed at least in part to human influence on the climate according to our analysis (purple cells, Fig. 2c). Using gridded population density data33, we calculate that this covers 85% of the world’s population. The majority of land grid cells show attributable warming trends, with exceptions where trends cannot be robustly distinguished from internal variability (white cells, category 0) or where there is insufficient data to establish trends (grey cells). For precipitation, attributable wetting and drying trends are found with greater geographical variation. There are also more grid cells where a trend in precipitation cannot be established, or where the observed trend is opposite in sign to that simulated by climate model historical simulations (green and yellow cells, +-4).
Though most of the world’s population resides in areas where trends in temperature and or precipitation can be at least partially attributed to human influence according to our analysis, there is substantial geographical variation in the degree to which the impacts of temperature and precipitation on human and natural systems have been studied. We characterise areas with fewer than 5 weighted studies per grid cell as displaying low evidence, areas with between 5 and 20 weighted studies as robust evidence, and areas with more than 20 weighted studies as high evidence.
For 48% of global land area (hosting 74% of global population), we find robust or high evidence of impacts on human and natural systems colocated with attributable temperature or precipitation trends (Fig. 2c). Areas with this combination of evidence are indicated by the darker purple cells. These constitute almost all grid cells in Western Europe, North America, South and East Asia, and there are parts of all continents for which we have similar pockets of substantial preliminary evidence.
However, for 33% of global land area (hosting 11% of global population), although we have evidence that long-term trends in precipitation and temperature are attributable at least in part to human influence, there is apparently relatively little evidence in the existing literature about how these trends impact human and natural systems (Fig. 2c lightest purple shading). This imbalance suggests, in line with research measuring climate impacts using remote sensing34, that the lack of evidence in individual studies is rather to do with these locations being less intensively studied than an absence of impacts in these areas. Parts of Western Africa, South-east, Western and Northern Asia contain several light red grid cells where there is evidence to suggest that the climate (temperature and/or precipitation) has changed because of human influence, but we have little evidence on how this may be impacting human and natural systems. These demonstrable evidence gaps suggest a lack of impacts research commensurate with current knowledge of how the local climate (temperature and/or precipitation) is changing.
Some of the spatial features can be explained by the geographical characteristics. Among the regions with limited evidence are vast, sparsely populated and difficult to reach areas with a comparable uniform biosphere and climate such as Siberia or the Saharan desert. But beyond these features, our results clearly reveal a substantial 'attribution gap'. We find that 23% of the population of low income countries live in areas with low impact evidence despite at least partially attributable trends in temperature and/or precipitation (Fig. 2.d). In high income countries, this figure is only 3%. A density of 5 studies per grid cell or more with attributable impacts is 1.76 times as prevalent by population for high income countries (88%) as for low income countries (50%), while a density of 20 studies or more with attributable impacts is more than 4 times as prevalent (81% compared to 17%).
In the remaining grey grid cells (Fig. 2c), trends in precipitation and temperature have not been attributed to human influence on the climate according to the methodology in refs. 18 and 19, as applied to CMIP6 models. This does not rule out the possibility that some trends in precipitation or temperature have occured in these regions that have been driven, at least in part, by human influence on the climate. However, due to various factors, such as lack of adequate observational data, high levels of natural variability compared to the climate change signal, or limitations in modelling or estimated climate forcings, some observed changes that actually include anthropogenic contributions may not yet be attributable at the grid cell level. This categorisation of individual gridpoints may well change as new observational data are collected, as models improve, as the global climate continues to warm, or as detection/attribution methodologies improve. Darker grey grid cells (10% of analyzed land area) indicate where there are no detectable trends in temperature or precipitation that can be attributed to human influence at a grid cell level, but where there nevertheless appears to be substantial evidence that local trends in some climate variables lead to impacts on human and natural systems. For example, many studies refer to the impacts of temperature in the state of Western Australia, but of the 40 grid cells in the state, an attributable temperature trend can be demonstrated for 22 cells. For 16 of the remaining cells a lack of data means that a detectable trend cannot be established, and for the remaining 2 cells, no attributable trend can be established.
The lightest grey cells (17% of land area) describe areas where we do not detect anthropogenic influence on regional temperature or precipitation and find few publications about the impacts of temperature or precipitation on human and natural systems in those areas. Apart from high latitudes and over the ocean, these cells are primarily in Africa. For example, in the light grey patch over the central part of sub-Saharan Africa, either limitations of observed data, models, or low signal to noise imply that we are unable to attribute temperature or precipitation trends to human influence on the climate using the methodologies employed here (see extended fig. 4); further, we have identified few studies analysing the impacts of climate change on human and natural systems in those regions. These evidence gaps constitute significant blind spots in our understanding of climate impacts, and in some cases in our understanding of attributable anthropogenic influence on regional precipitation and/or temperature.
In total, 57,366 studies discuss impacts related to a driver which our analysis suggests can be attributed in part to human influence on the climate in at least one grid cell to which the study refers. We find hundreds of partially or mostly attributable studies (where there are attributable trends in the relevant climate variable for at least 1% or more than 50% of grid cells respectively) in each impact category across all continents (Fig. 3, indicated by the darker green and purple bars). This figure ranges from 268 (143-514) studies of impacts on mountains, snow and ice in Africa to 7,835 (4,308-13,552) studies of impacts on terrestrial ecosystems in North America. Wide confidence intervals here reflect the compound uncertainty deriving from classification of relevance, impact and driver.
Our analysis also allows quantification of how the share of research on each impact category varies from continent to continent. For example, research on human and managed systems makes up 12% of all research globally, but only 10% of research in Europe, compared to 19% in Africa. This focus on human and managed systems in Africa is remarkable given that the absolute numbers of studies in Africa (1,466) is similar to that in Europe (1,799) despite the vast difference in total numbers of studies between the two continents. This greater share of research in Africa documents impacts in human and managed systems may reflect the high vulnerability of particularly sub-Saharan Africa to climate impacts35.