CL3.2.3

Probability distribution functions (PDFs) are widely used in projections of future climate, projections of the impacts of future climate, and by climate services aiming to provide information to support practical climate change adaptation. Furthermore they are often used as a means of connecting these different activities and linking the variety of disciplines involved in climate science and climate social science.

Here we present an assessment of when such probability distributions misrepresent our uncertainty and a discussion of how we might recognise when such misrepresentations occur [1]. We go on to provide a collection of alternatives to probability distributions for use in such situations.

We start by categorising the ways that probability distributions can misrepresent the state of our knowledge about future climate. Such misrepresentation is of importance because it may adversely affect practical societal decisions, particularly in regard to adaptation activities, as well as misdirecting other research efforts.

We follow this with a discussion of how we might identify such misrepresentations. Doing so would help us communicate climate information better and consequently provided better reasoned and more robust scientific conclusions and societal decisions. Such assessments are an important component in the evaluation of climate information provided by climate services: what aspects of the information can be described as actionable.

We consider two perspectives on these issues. On one, available theory and evidence in climate science essentially excludes using probability distributions to represent our uncertainty. On the other, which represents a significant strand of current practice, probability distributions can legitimately be provided by relying on appropriate expert judgement and the recognition of associated risks.  We discuss the reasoning behind each perspective, framed in terms of the analysis of climate models and expert judgement.

Finally we explore alternatives to the use of probability distributions. We describe two formal alternatives, namely imprecise probabilities and possibilistic distribution functions, as well as some informal possibilistic alternatives. We suggest that the possibilistic alternatives are preferable.

[1] Katzav, Thompson, Risbey, Stainforth, Bradley and Frisch, On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives, Climatic Change, 2021.

How to cite: Thompson, E., Katzav, J., Risbey, J., Stainforth, D., Bradley, S., and Frisch, M.: On the use, misuse and alternatives to probability distributions in descriptions of future climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2356, https://doi.org/10.5194/egusphere-egu22-2356, 2022.

Climate change brings unprecedented risks to the future stability of global financial systems and our society. Central Banks and Governments around the world have joined forces to shape new policy in response to risks driven by climate change. These regulations will require firms to proactively identify, model, quantify and manage climate-related risks for the first time.

Climate X have just completed their first of a kind Integrated Assessment Model evaluating asset-level impacts of climate-related hazards across all 22 million addressed buildings in the UK. Each building has a modelled probability, severity and a simple A-F climate rating as well as a projected loss under given scenario.  

Our geospatial core comprises of UK-specific physical risk models including flooding (pluvial, coastal, fluvial) and geohazards (subsidence and landslides). The models are at 90mx90m resolution feed UKCP18 climate scenarios of RCP8.5 and RCP2.6. Flood models are physics-based and reach a Critical Success Index (CSI) of 75%+. Geohazard models use a combination of DinSAR and machine learning modelling with 90%+ accuracy levels (measured by AUC).

Loss models combine the geospatial hazards with buildings’ exposure (square meterage) and respective vulnerabilities: age, use, material built etc. They then apply insurance-based damage curves to compute structure & content losses against building replacement costs.

How to cite: Kluza, K. and the Climate X: Climate X: Projecting losses due to extreme weather events linked to climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8342, https://doi.org/10.5194/egusphere-egu22-8342, 2022.

This study examines the effects of (extreme) weather conditions on the willingness to purchase and on actual purchase of land ownership rights in Uganda. We use three waves of the Uganda National Panel Survey in combination with high-resolution gridded precipitation and temperature data, with which we calculate a drought index as weather shock measure, the Standardized Precipitation Evapotranspiration Index (SPEI). Using a household fixed-effects approach, we exploit spatial and temporal variation in SPEI values to causally identify the effect of extreme weather events on the willingness to acquire ownership to land and actual changes in the land ownership structure of households over time. Results show that dry conditions dampen households’ intentions to purchase land ownership rights, while wet conditions positively affect such intentions. In addition, wet conditions substantially increase the price households are willing to pay to purchase land ownership. The effects are robust to different specifications, persistent over time and translate into actual changes of land ownership ratios with a two-year time lag. The findings suggest that more favourable climatic conditions for agriculture increase interest in land ownership, which has implications for land formalisation programmes and climate change adaptation efforts.

How to cite: Murken, L., Krähnert, K., and Gornott, C.: Extreme weather effects on land ownership in Uganda, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12655, https://doi.org/10.5194/egusphere-egu22-12655, 2022.

The relative lack of research concerning the potential impacts of climate change on different sectors in developing countries, especially the Middle Eastern countries, as the essential prerequisite of climate policy actions has made these countries the frontline against climate impacts. To fill this gap, we use a non-market valuation model to assess the future impacts of climate change on agriculture in Iran. In this study, the relationship between farmland net revenue, as a proxy for land values, and climate change is investigated using a long-spanning Ricardian framework. For farm variables, we take into account novel hydro-climatic variables and climate extreme indices by taking advantage of a high-resolution meteorological dataset to tackle the sparse distribution of weather stations in Iran. For non-farm variables, we consider the pressure of rapid urbanization and migrations from rural areas in addition to socio-economic variables. This study also contributes to the body of literature through methodological improvement by taking advantage of spatial panel econometrics to develop a more robust and consistent model against spatial dependency, spatial heterogeneity, and omitted factors extraneous to the agriculture sector. The estimated coefficients are then employed in projecting long-run welfare impacts on the agricultural sector under several climate change scenarios based on the sixth phase of the Coupled Model Intercomparison Project (CMIP6) in 2050 and 2080. The results show that although climate change probably could have deleterious impacts on agriculture when we see the whole picture, its impacts would highly depend on climate zones and geographical locations. Generally, counties in snow and warm-temperate climate classes would be less susceptible to climate impacts than arid and semi-arid counties. Besides, climate change could even be beneficial for agriculture in a few counties owing to a decrease in cold extreme events frequency and intensity and an increase in growing season lengths and effective growing season degree-days. Thus, we suggest that these positive factors of climate change should be included in empirical studies to avoid overestimating the disruptive impacts of climate change. Finally, we argue that overlooking spatial dependency and spatial heterogeneity in Ricardian models could substantially affect impact assessments.

How to cite: Malaekeh, S., Shiva, L., and Safaie, A.: Climate change impacts on agriculture: do spatial spillovers matter?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4016, https://doi.org/10.5194/egusphere-egu22-4016, 2022.

Given widespread and serious implications of increasingly intense and frequent extreme events such as droughts and heat waves, there is a growing demand for integrated approaches capable of supporting prospective risk reduction through better adaption to a changing climate. While anticipating future impacts is essential to inform policy decisions, there is simultaneous need for projections at higher spatial resolution to forecast the local effects of global change. Here, we present a spatial multi-agent system (MAS) model, DroughtMAS, calibrated using a positive mathematical programming (PMP) approach. The model simulates land-use adaptation to future drought conditions, estimates the economic damages of future droughts, and assesses policy measures aimed at enhancing the drought resilience of German agriculture. It represents the biophysical and agro-economic heterogeneity of German agriculture through 23,396 individually parameterized land-user agents located on a country-wide 4x4km grid. Cropping behavior is calibrated with land-use data from high-resolution remote sensing analyses and public records and validated with independent land-use data. The economic parameters ground the model to a policy-relevant context while the statistical yield functions capture the impacts of biophysical factors on crop production. These yield functions enable the model to respond to soil moisture changes from observed data or projections from hydrological models. DroughtMAS extends the classical PMP model to capture short-run responses to droughts more realistically and analyze how fast the farmers move towards the desirable equilibrium conditions under recurring droughts. The model shows that farmers gradually adapt to prolonged drought conditions, with a lower degree of adaptation in the first drought years only slightly mitigating drought impacts. We present first analysis of future drought scenarios to demonstrate the ability of the model to quantify risks from potential droughts across Germany in monetary terms. The results provide bottom-up estimates of economic damages of droughts accounting for much needed short-run behavioral dynamics of adaptation. This provides valuable and realistic projections of future drought impacts of farm-specific changes aggregated at national scale. The model also presents spatiotemporal pattern of these impacts, showing the potential for such projections to inform targeted policy interventions. DroughtMAS provides a platform that can be extended to capture additional adaptation behaviors (e.g. drought-resilient crops, adapted crop calendars, irrigation systems) and combined with other models that require empirically validated inputs about various agricultural decision-making conditions.

How to cite: Nagpal, M., Heilemann, J., Klassert, C., Peichl, M., Klauer, B., and Gawel, E.: Simulating economic impacts of droughts on German agriculture using the country-scale Multi-Agent System model DroughtMAS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9157, https://doi.org/10.5194/egusphere-egu22-9157, 2022.

There is growing macro-level evidence about the negative and non-linear impact of temperature on aggregate economic output, with the temperature effects varying widely across geographical regions and extending to both agricultural and non-agricultural sectors (Hsiang, 2010; Dell et al., 2012; Burke et al.,  2015). Less is known about the country-specific micro-level mechanisms behind the temperature-output relationship and their role in adaptation to the warming climate. Addressing this knowledge gap is of considerable importance for designing effective climate change policies, especially in developing countries, which are generally exposed to higher temperatures and have limited capacity to adapt to a changing climate (Somanathan et al., 2021). This paper contributes to the progress on this issue by estimating the impact of temperature on the output of manufacturing plants in India and decomposing it into the effects on TFP and factor inputs.

The paper combines a plant-level panel of detailed production data from the formal manufacturing sector in India over 1998-2007 with high-resolution satellite-based meteorological and pollution datasets, merged at the district level. I use two approaches to construct temperature variable: a standard in the literature binned-variable and seasonal-variable approaches, both used in empirical specifications in contemporaneous and lagged forms. To isolate the role of temperature more clearly, I account for simultaneous variations in temperature and a rich set of weather and pollution controls. I further minimize the estimation biases by including plant-specific and year-by-two-digit-industry fixed effects. Standard errors are clustered at plant and district-year levels to address spatial and serial correlation.

My main findings are two-fold. First, the relationship between temperature and manufacturing output is non-linear. The output losses are especially large during the hottest season and at extreme temperatures, with more substantial losses occurring at low rather than high temperatures. This finding is consistent with the theoretical prediction from Nath (2021). An additional day with a temperature above 33°C decreases output by 0.12% or $3,749 relative to a day in the optimal interval. The comparable estimate for an additional day with a temperature below 8°C is a decrease of 0.27% and $8,435, respectively. Second, the estimated temperature-output relationship is driven by the joint effects of temperature on TFP and capital, contributing roughly 30% and 70%. The response of TFP to temperature closely follows the response of output, while the response of capital mirrors the response of output only to higher temperatures. I further decompose these primary channels to show that temperature affects TFP through its impact on labor productivity, and machinery is the most suitable for adaptation category of capital. I also find suggestive evidence of labor reallocation between seasonal manufacturing industries and between economic sectors.

These findings have important implication for adaptation. Manufacturing sector in India can adapt to changing climate by reducing the sensitivity of labor productivity to temperature and by investing in capital, prioritizing investments in machinery. Labor-related adjustments can contribute to adaptation by offsetting direct productivity losses or facilitating labor reallocation. Patterns of the seasonal responses and timing of the adjustments’ effects should also be taken into account.

How to cite: Kyrychenko, O.: Decomposition of the temperature-driven output losses in India: Plant-level evidence for the climate change adaptation policy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12652, https://doi.org/10.5194/egusphere-egu22-12652, 2022.

Decarbonisation of energy technologies is essential to meet climate change targets, however, this process has the potential to generate new or further emphasize pre-existing inequalities within society. By ensuring a low carbon energy transition is sustainable and equitable, trade-offs and co-benefits between decarbonisation and other U.S. policy objectives can be achieved. This is important as many communities are in the process of developing or updating their climate action plans (CAPs). The ‘success’ of a CAP is often measured against the greenhouse gas emission forecast for a baseline year and does not consider the wider implications in terms of environmental impacts or impacts to the individuals it directly affects.

We present a theoretical framework to aid decision makers to ensure energy justice is incorporated when designing CAPs. This framework expands on several key principles incorporated into the tenets of energy justice. These principles include energy availability, reliability and affordability, high-quality employment, access to information, objective governance, intersectional responsibility, intra-generational equity, intergenerational equity, and due process. This framework aims to reduce the disproportional burdens of transitioning towards a low carbon energy future by understanding why these key principles should be integrated into new and amended CAPs.

How to cite: Logan, K. G., Keith, L., Gupta, N., Leinberger, A. J., Shelton, R., and Jacobs, K. L.: Integrating energy justice with community climate action planning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12961, https://doi.org/10.5194/egusphere-egu22-12961, 2022.