Marine diazotrophs fix dinitrogen gas into bioavailable nitrogen that drives the ocean nitrogen cycle; yet, efforts to infer global diazotroph distributions have been limited by a sparsity of observations. In situ measurements of nifH gene abundance (essential for nitrogen fixation) are increasingly being used to inform the biogeography of diazotrophs. However, comparing such gene abundances spatially, temporally and between diazotroph species remains difficult. We synthesize existing data on gene-to-cell and cell-to-biomass conversions for four major diazotroph groups to convert nifH gene counts to abundance-and biomass-based biogeographic "currencies." Results suggest up to two orders of magnitude uncertainty converting from nifH gene abundance to cell abundance, and up to four orders of magnitude uncertainty from nifH gene abundance to biomass. Uncertainty arises due to large taxonomic variation in cell size and presumed polyploidy, that is, variability in the number of genomes per cell. Such uncertainties hinder comparing biogeographies of different species. Additionally, numerical models need biogeographies for validation, typically in the currency of carbon biomass. Here, we show that conversion uncertainty from nifH gene abundance to biomass overwhelms biomass variability simulated in such models. These results demonstrate a basic currency problem in converting gene abundance observations to biogeographically meaningful quantities for synthesizing studies and modeling approaches. Such issues may also have relevance to other genes and organisms beyond diazotrophs. To avoid biases in interpreting gene counts as a measure of abundance, we suggest converting gene counts to a binary presence/ non-detect metric to map broad biogeographical distributions more robustly.

Tropical cyclones (TCs) cause devastating damage to life and property. Historical TC data is scarce, complicating adequate TC risk assessments. Synthetic TC models are specifically designed to overcome this scarcity. While these models have been evaluated on their ability to simulate TC activity, no study to date has focused on model performance and applicability in TC risk assessments. This study performs the intercomparison of four different global-scale synthetic TC datasets in the impact space, comparing impact return period curves, probability of rare events, and hazard intensity distribution over land. We find that the model choice influences the costliest events, particularly in basins with limited TC activity. Modelled direct economic damages in the North Indian Ocean, for instance, range from 40 to 246 billion USD for the 100-yr event over the four hazard sets. We furthermore provide guidelines for the suitability of the different synthetic models for various research purposes.

Abstract. Modelling the risk of natural hazards for society, ecosystems, and the economy is subject to strong uncertainties, even more so in the context of a changing climate, evolving societies, growing economies, and declining ecosystems. Here, we present a new feature of the climate-risk modelling platform CLIMADA (CLIMate ADAptation), which allows us to carry out global uncertainty and sensitivity analysis. CLIMADA underpins the Economics of Climate Adaptation (ECA) methodology which provides decision-makers with a fact base to understand the impact of weather and climate on their economies, communities, and ecosystems, including the appraisal of bespoke adaptation options today and in future. We apply the new feature to an ECA analysis of risk from tropical cyclone storm surge to people in Vietnam to showcase the comprehensive treatment of uncertainty and sensitivity of the model outputs, such as the spatial distribution of risk exceedance probabilities or the benefits of different adaptation options. We argue that broader application of uncertainty and sensitivity analysis will enhance transparency and intercomparison of studies among climate-risk modellers and help focus future research. For decision-makers and other users of climate-risk modelling, uncertainty and sensitivity analysis has the potential to lead to better-informed decisions on climate adaptation. Beyond provision of uncertainty quantification, the presented approach does contextualize risk assessment and options appraisal, and might be used to inform the development of storylines and climate adaptation narratives.