ALBERT

Description: Comparing temporal and spatial vegetation changes between reconstructions or between reconstructions and model simulations requires carefully selecting an appropriate evaluation metric. A common way of comparing reconstructed and simulated vegetation changes involves measuring the agreement between pollen- or model-derived unary vegetation estimates, such as the biome or plant functional type (PFT) with the highest affinity scores. While this approach based on summarising the vegetation signal into unary vegetation estimates performs well in general, it overlooks the details of the underlying vegetation structure. However, this underlying data structure can influence conclusions since minor variations in pollen percentages modify which biome or PFT has the highest affinity score (i.e. modify the unary vegetation estimate). To overcome this limitation, we propose using the Earth mover's distance (EMD) to quantify the mismatch between vegetation distributions such as biome or PFT affinity scores. The EMD circumvents the issue of summarising the data into unary biome or PFT estimates by considering the entire range of biome or PFT affinity scores to calculate a distance between the compared entities. In addition, each type of mismatch can be given a specific weight to account for case-specific ecological distances or, said differently, to account for the fact that reconstructing a temperate forest instead of a boreal forest is ecologically more coherent than reconstructing a temperate forest instead of a desert. We also introduce two EMD-based statistical tests that determine (1) if the similarity of two samples is significantly better than a random association given a particular context and (2) if the pairing between two datasets is better than might be expected by chance. To illustrate the potential and the advantages of the EMD as well as the tests in vegetation comparison studies, we reproduce different case studies based on previously published simulated and reconstructed biome changes for Europe and capitalise on the advantages of the EMD to refine the interpretations of past vegetation changes by highlighting that flickering unary estimates, which give an impression of high vegetation instability, can correspond to gradual vegetation changes with low EMD values between contiguous samples (case study 1). We also reproduce data-model comparisons for five specific time slices to identify those that are statistically more robust than a random agreement while accounting for the underlying vegetation structure of each pollen sample (case study 2). The EMD and the statistical tests are included in the paleotools R package (https://github.com/mchevalier2/paleotools, last access: 3 May 2023).

Description: The upper Indus River basin is characterized by biseasonal heavy precipitation falling on the foothills of major mountain ranges (Hindu Kush, Karakorm, Himalayas). Numerical studies have confirmed the importance of topography for the triggering of precipitation and investigated the processes responsible for specific events, but a systematic and cross-seasonal analysis has yet to be conducted. Using ERA5 reanalysis data and statistical methods, we show that more than 80% of the precipitation variability is explained by southerly moisture transport at 850 and 700 hPa, along the Himalayan foothills. We conclude that most of the precipitation is generated by the forced uplift of a cross-barrier flow. This process explains both wet seasons, despite different synoptic conditions, but is more important in winter. The precipitation signal is decomposed into the contribution of each altitude and each variable (wind and moisture), which exhibit different seasonality. The winter wet season is dominated by moisture transport at higher altitude, and is triggered by an increase in wind. By contrast, the summer wet season is explained by an increase in moisture at both altitudes, while wind is of secondary importance. Selected CMIP6 climate models are able to represent the observed links between precipitation and southerly moisture transport, despite important seasonal biases that are due to a misrepresentation of the seasonality in the magnitude of the southerly wind component.

Description: Large uncertainty remains about the amount of precipitation falling in the Indus River basin, particularly in the more mountainous northern part. While rain gauge measurements are often considered as a reference, they provide information for specific, often sparse, locations (point observations) and are subject to underestimation, particularly in mountain areas. Satellite observations and reanalysis data can improve our knowledge but validating their results is often difficult. In this study, we offer a cross-validation of 20 gridded datasets based on rain gauge, satellite, and reanalysis data, including the most recent and less studied APHRODITE-2, MERRA2, and ERA5. This original approach to cross-validation alternatively uses each dataset as a reference and interprets the result according to their dependency on the reference. Most interestingly, we found that reanalyses represent the daily variability of precipitation as well as any observational datasets, particularly in winter. Therefore, we suggest that reanalyses offer better estimates than non-corrected rain-gauge-based datasets where underestimation is problematic. Specifically, ERA5 is the reanalysis that offers estimates of precipitation closest to observations, in terms of amounts, seasonality, and variability, from daily to multi-annual scale. By contrast, satellite observations bring limited improvement at the basin scale. For the rain-gauge-based datasets, APHRODITE has the finest temporal representation of the precipitation variability, yet it importantly underestimates the actual amount. GPCC products are the only datasets that include a correction factor of the rain gauge measurements, but this factor likely remains too small. These findings highlight the need for a systematic characterisation of the underestimation of rain gauge measurements.

Description: Large uncertainty remains about the amount of precipitation falling in the Indus River basin, particularly in the more mountainous northern part. While rain gauge measurements are often considered as a reference they are only punctual and subject to underestimation. Satellite observations and reanalysis output can improve our knowledge but validating their results is often difficult. In this study, we offer a cross-validation of 20 gridded datasets based on rain gauge, satellite and reanalysis, including the most recent and little studied APHRODITE-2, MERRA2, and ERA5. This original approach to cross-validation alternatively uses each dataset as a reference and interprets the result according to their dependency with the reference. Most interestingly, we found that reanalyses represent the daily variability as well as any observational datasets, particularly in winter. Therefore, we suggest that reanalyses offer better estimates than non-corrected rain gauge-based datasets where underestimation is problematic. Specifically, ERA5 has proven to be the most able reanalysis for representing the amounts of precipitation as well as its variability from daily to multi-annual scale. By contrast, satellite observations bring limited improvement at the basin scale. For the rain gauge-based datasets, APHRODITE has the finest representation of the precipitation variability, yet importantly it underestimates the actual amount. GPCC products are the only datasets that include a correction of the measurements but remain likely too small. These findings highlight the need for a systematic characterisation of the underestimation of rain gauge measurements.

Description: Comparing temporal and spatial vegetation changes between reconstructions or between reconstructions and model simulations requires carefully selecting an appropriate evaluation metric. A common way of comparing reconstructed and simulated vegetation changes involves measuring the agreement between pollen- or model-derived unary vegetation estimates, such as the biome or plant functional type (PFT) with the highest affinity scores. While this approach based on summarising the vegetation signal into unary vegetation estimates performs well in general, it overlooks the details of the underlying vegetation structure. However, this underlying data structure can influence conclusions since minor variations in pollen percentages modify which biome or PFT has the highest affinity score (i.e. modify the unary vegetation estimate). To overcome this limitation, we propose using the Earth mover's distance (EMD) to quantify the mismatch between vegetation distributions such as biome or PFT affinity scores. The EMD circumvents the issue of summarising the data into unary biome or PFT estimates by considering the entire range of biome or PFT affinity scores to calculate a distance between the compared entities. In addition, each type of mismatch can be given a specific weight to account for case-specific ecological distances or, said differently, to account for the fact that reconstructing a temperate forest instead of a boreal forest is ecologically more coherent than reconstructing a temperate forest instead of a desert. We also introduce two EMD-based statistical tests that determine (1) if the similarity of two samples is significantly better than a random association given a particular context and (2) if the pairing between two datasets is better than might be expected by chance. To illustrate the potential and the advantages of the EMD as well as the tests in vegetation comparison studies, we reproduce different case studies based on previously published simulated and reconstructed biome changes for Europe and capitalise on the advantages of the EMD to refine the interpretations of past vegetation changes by highlighting that flickering unary estimates, which give an impression of high vegetation instability, can correspond to gradual vegetation changes with low EMD values between contiguous samples (case study 1). We also reproduce data–model comparisons for five specific time slices to identify those that are statistically more robust than a random agreement while accounting for the underlying vegetation structure of each pollen sample (case study 2). The EMD and the statistical tests are included in the paleotools R package (https://github.com/mchevalier2/paleotools, last access: 3 May 2023).