Abstract. South West Western Australia (SWWA) has experienced a prolonged reduction in rainfall in recent decades, with associated reductions in regional water supply and residential and agricultural impacts. The cause of the reduction has been widely considered but remains unclear. The relatively short length of the instrumental record limits long-term investigation. A previous proxy-based study used a statistically negative correlation between SWWA rainfall and snowfall from the Dome Summit South (DSS) ice core drilling site, Law Dome, East Antarctica, and concluded that the anomaly of recent decades is unprecedented over the ∼ 750-year period of the study (1250–2004 CE). Here, we extend the snow accumulation record to cover the period from 22 BCE to 2015 CE and derive a rainfall reconstruction over this extended period. This extended record confirms that the recent anomaly is unique in the period since 1250 CE and unusual over the full ∼ 2000-year period, with just two other earlier droughts of similar duration and intensity. The reconstruction shows that SWWA rainfall started to decrease around 1971 CE. Ensembles of climate model simulations are used to investigate the potential roles of natural variability and external climate drivers in explaining changes in SWWA rainfall. We find that anthropogenic greenhouse gases are likely to have contributed towards the SWWA rainfall drying trend after 1971 CE. However, natural variability may also have played a role in determining the timing and magnitude of the reduction in rainfall.
<p><strong>Global mean sea level (GMSL) is projected to continue rising this century, potentially impacting up to 1 billion people by 2050 (Lee et al., 2023). Antarctica, as the Earth’s largest ice reservoir with a sea level equivalent volume of around 58 meters (Morlighem et al., 2020), could significantly impact the magnitude of future sea level rise. However, how much sea level rise will be caused by the Antarctic Ice Sheet (AIS) is highly uncertain (Rintoul et al., 2018), partly because of unclear future stability of Antarctic ice shelves. Surface melt has been identified as a crucial factor contributing to ice shelf collapse (Rott et al., 1996; van den Broeke, 2005; Trusel et al., 2015) through mechanisms of hydrofracturing (Lai et al., 2020). Projections have shown that the magnitude of surface melt will increase and the melt extent will be widespread (Trusel et al., 2015; Gilbert and Kittel, 2021). However, the distribution of future surface melt is not well known at high spatial resolutions. This is because climate models that employ comprehensive surface energy balance (SEB) schemes are too computationally expensive to run at fine resolutions (van den Broeke et al., 2023). By contrast, temperature-index models, such as the positive degree-day (PDD) model, are computationally efficient and have been utilized for snowmelt estimation for more than 90 years (Rango and Martinec, 1995), offering an alternative approach for future melt projections. However, the PDD parameters commonly used for AIS modelling are typically based on those derived for the Greenland Ice Sheet. An assessment of the viability of the PDD modelling approach for AIS surface melt projections has not yet been conducted, and the accuracy of the PDD model in estimating surface melting on the AIS remains unclear.</strong></p><p>This thesis first comprehensively assesses the PDD model for estimating surface melt on the AIS. The results from the assessment show that a PDD model with spatially-uniform parameters, when compared to estimates of surface melt days from satellites and surface melt rates from regional climate models over the past four decades, lacks accuracy in reconstructing AIS surface melt. Therefore, in order to improve the accuracy of the PDD model for AIS surface melt projections, I develop a novel grid-cell-level spatially-distributed PDD model by minimizing the error with respect to satellite estimates and SEB model outputs on each individual computing cell (minimal RMSE approach) for the past four decades. Evaluations of this PDD model demonstrate the robustness of the minimal RMSE approach and the applicability of the PDD model to warmer climate scenarios. To calculate future melting, I incorporate 100-meter-resolution topographic variability to downscale forcing temperature fields derived from ERA5, CMIP5, and CMIP6. The resultant 100-meter-resolution AIS surface melt projections show that the Larsen-C, Shackleton, Thwaites, and Totten ice shelves will all be at high risk of collapse this century due to increased surface melt if emissions follow the SSP3-7.0 pathway. Trajectories of latitudinal melt migration calculated from these high resolution AIS surface melt projections suggest that SSP1-2.6 is likely the only emissions pathway under which future AIS surface melt can be stabilized at present levels.</p>
Abstract. Surface melt is one of the primary drivers of ice shelf collapse in Antarctica. Surface melting is expected to increase in the future as the global climate continues to warm, because there is a statistically significant positive relationship between air temperature and melt. Enhanced surface melt will negatively impact the mass balance of the Antarctic Ice Sheet (AIS) and, through dynamic feedbacks, induce changes in global mean sea level (GMSL). However, current understanding of surface melt in Antarctica remains limited in past, present or future contexts. Continental-scale spaceborne observations of surface melt are limited to the satellite era (1979–present), meaning that current estimates of Antarctic surface melt are typically derived from surface energy balance (SEB) or positive degree-day (PDD) models. SEB models require diverse and detailed input data that are not always available and require considerable computational resources. The PDD model, by comparison, has fewer input and computational requirements and is therefor suited for exploring surface melt scenarios in the past and future. The use of PDD schemes for Antarctic melt has been less extensively explored than their application to surface melting of the Greenland Ice Sheet, particularly in terms of a spatially-varying parameterization. Here, we construct a PDD model, force it only with 2-m air temperature reanalysis data, and parameterize it by minimizing the error with respect to satellite observations and SEB model outputs over the period 1979 to 2022. We compare the spatial and temporal variability of surface melt from our PDD model over the last 43 years with that of satellite observations and SEB simulations. We find that the PDD model can generally capture the same spatial and temporal surface melt patterns. Although there were at most four years over/under- estimation on ice shelf regions in the epoch, these discrepancies reduce when considering the whole AIS. With the limitations discussed, we suggest that an appropriately parameterized PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.
Abstract. Surface melting is one of the primary drivers of ice shelf collapse in Antarctica and is expected to increase in the future as the global climate continues to warm because there is a statistically significant positive relationship between air temperature and melting. Enhanced surface melt will impact the mass balance of the Antarctic Ice Sheet (AIS) and, through dynamic feedbacks, induce changes in global mean sea level (GMSL). However, the current understanding of surface melt in Antarctica remains limited in terms of the uncertainties in quantifying surface melt and understanding the driving processes of surface melt in past, present and future contexts. Here, we construct a novel grid-cell-level spatially distributed positive degree-day (PDD) model, forced with 2 m air temperature reanalysis data and spatially parameterized by minimizing the error with respect to satellite estimates and surface energy balance (SEB) model outputs on each computing cell over the period 1979 to 2022. We evaluate the PDD model by performing a goodness-of-fit test and cross-validation. We assess the accuracy of our parameterization method, based on the performance of the PDD model when considering all computing cells as a whole, independently of the time window chosen for parameterization. We conduct a sensitivity experiment by adding ±10 % to the training data (satellite estimates and SEB model outputs) used for PDD parameterization and a sensitivity experiment by adding constant temperature perturbations (+1, +2, +3, +4 and +5 ∘C) to the 2 m air temperature field to force the PDD model. We find that the PDD melt extent and amounts change analogously to the variations in the training data with steady statistically significant correlations and that the PDD melt amounts increase nonlinearly with the temperature perturbations, demonstrating the consistency of our parameterization and the applicability of the PDD model to warmer climate scenarios. Within the limitations discussed, we suggest that an appropriately parameterized PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.
Abstract Projections show that Antarctic surface melt will increase through the current century, potentially accelerating ice shelf collapse and global sea level rise [1–3]. However, a high-resolution map of projected melt is currently lacking, which limits the accuracy of predictions of hydrofracturing [2] and loss of buttressing to grounded ice [4]. Here, we present 100-meter projections of Antarctic surface melt potential under Shared Socio-economic Pathways (SSPs) 1-2.6, 2-4.5, 3-7.0, and 5-8.5. These projections are generated from a spatially-distributed statistical model [5] trained on 30 years of observational data, and uses high-resolution topographic data [6] to downscale forcing temperature fields. Local temperature corrections arising from this downscaling (but typically absent from general circulation models) are up to 6°C, comparable to global temperature change under the highest greenhouse gas emissions scenarios by 2100 [7]. Using these more accurate temperature fields our high-resolution downscaled melt calculations show that Larsen-C, Shackleton, Thwaites, Getz, and Totten ice shelves will all be at high risk of collapse from mid-century due to increased surface melt, even if emissions follow the mid-range SSP3-7.0 pathway. Future trajectories of latitudinal melt migration show that SSP1-2.6 is the only emissions pathway under which future Antarctic surface melt is stabilized at present levels.
Version 2:Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added.https://doi.org/10.5194/tc-17-3667-2023 Version 1: This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere. This dataset contains annual PDD model output.
Version 2:Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added.https://doi.org/10.5194/tc-17-3667-2023 Version 1: This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere. This dataset contains annual PDD model output.
This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere. This dataset contains annual PDD model output.
Abstract. Surface melt is one of the primary drivers of ice shelf collapse in Antarctica. Surface melting is expected to increase in the future as the global climate continues to warm, because there is a statistically significant positive relationship between air temperature and melt. Enhanced surface melt will negatively impact the mass balance of the Antarctic Ice Sheet (AIS) and, through dynamic feedbacks, induce changes in global mean sea level (GMSL). However, current understanding of surface melt in Antarctica remains limited in past, present or future contexts. Continental-scale spaceborne observations of surface melt are limited to the satellite era (1979–present), meaning that current estimates of Antarctic surface melt are typically derived from surface energy balance (SEB) or positive degree-day (PDD) models. SEB models require diverse and detailed input data that are not always available and require considerable computational resources. The PDD model, by comparison, has fewer input and computational requirements and is therefor suited for exploring surface melt scenarios in the past and future. The use of PDD schemes for Antarctic melt has been less extensively explored than their application to surface melting of the Greenland Ice Sheet, particularly in terms of a spatially-varying parameterization. Here, we construct a PDD model, force it only with 2-m air temperature reanalysis data, and parameterize it by minimizing the error with respect to satellite observations and SEB model outputs over the period 1979 to 2022. We compare the spatial and temporal variability of surface melt from our PDD model over the last 43 years with that of satellite observations and SEB simulations. We find that the PDD model can generally capture the same spatial and temporal surface melt patterns. Although there were at most four years over/under- estimation on ice shelf regions in the epoch, these discrepancies reduce when considering the whole AIS. With the limitations discussed, we suggest that an appropriately parameterized PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.
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