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Projecting how the East Asian summer monsoon (EASM) rainfall will change with global warming is essential for human sustainability. Reconstructing Holocene climate can provide critical insight into its forcing and future variability. However, quantitative reconstructions of Holocene summer precipitation are lacking for tropical and subtropical China, which is the core region of the EASM influence. Here we present high-resolution annual and summer rainfall reconstructions covering the whole Holocene based on the pollen record at Xinjie site from the lower Yangtze region. Summer rainfall was less seasonal and ~ 30% higher than modern values at ~ 10–6 cal kyr BP and gradually declined thereafter, which broadly followed the Northern Hemisphere summer insolation. Over the last two millennia, however, the summer rainfall has deviated from the downward trend of summer insolation. We argue that greenhouse gas forcing might have offset summer insolation forcing and contributed to the late Holocene rainfall anomaly, which is supported by the TraCE-21 ka transient simulation. Besides, tropical sea-surface temperatures could modulate summer rainfall by affecting evaporation of seawater. The rainfall pattern concurs with stalagmite and other proxy records from southern China but differs from mid-Holocene rainfall maximum recorded in arid/semiarid northern China. Summer rainfall in northern China was strongly suppressed by high-northern-latitude ice volume forcing during the early Holocene in spite of high summer insolation. In addition, the El Niño/Southern Oscillation might be responsible for droughts of northern China and floods of southern China during the late Holocene. Furthermore, quantitative rainfall reconstructions indicate that the Paleoclimate Modeling Intercomparison Project (PMIP) simulations underestimate the magnitude of Holocene precipitation changes. Our results highlight the spatial and temporal variability of the Holocene EASM precipitation and potential forcing mechanisms, which are very helpful for calibration of paleoclimate models and prediction of future precipitation changes in East Asia in the scenario of global warming.

The interpretation of stalagmite δ18O in terms of reflecting Asian summer monsoon (ASM) precipitation is still elusive. Here, we present high‐resolution stalagmite trace element ratios (X/Ca, X = Mg, Sr, Ba) records from southwest China covering 116.09 to 4.07 ka BP. δ18O, δ13C, and X/Ca values exhibit clear precessional cycles, with δ18O values reflecting ASM circulation/intensity, while X/Ca ratios capture local precipitation or evapotranspiration variations. Our results show that Northern Hemisphere summer insolation (NHSI) is the main driver of ASM intensity and precipitation phase variation, but global ice volume modulates the response magnitude of summer precipitation to insolation. During the Last Glacial Maximum, high ice volumes caused significant monsoon precipitation to decrease. In contrast to modern observations of the tripolar distribution of precipitation in China, our record is consistent with paleo‐precipitation records in southern and northern China. Plain Language Summary While it is well known that global changes have led to variations in the intensity and spatial distribution of Asian monsoon precipitation, the mechanisms behind this are not well understood. Paleoclimate records are essential for revealing the drivers behind monsoon variation. However, speleothem records from the Asian monsoon region rarely provide direct information on the amount of rainfall. Here we report on multiple indicator data sets from a stalagmite in southwestern China. It could help explore the variation of monsoon precipitation over the last ∼100,000 years. We find that the increase/decrease of Northern Hemisphere summer insolation controls the increase/decrease of Asian summer monsoon rainfall. In addition, global ice volume moderates the magnitude of rainfall response to insolation, and precipitation decreases significantly during high ice volume periods. Based on the present paleo‐precipitation records evidence, the existence of the spatial pattern of increasing/decreasing rainfall in central China corresponding to decreasing/increasing rainfall in northern and southern China remains ambiguous on the orbital scales, although the feature has been captured by some of the model simulations. Key Points Stalagmite trace elements are indicators of regional hydrological environmental variations in Southwestern China Northern Hemisphere summer insolation and global ice volume modulate the phase and amplitude variations of regional hydrological environment The meridional tripolar spatial pattern of precipitation in monsoon region in China on the orbital scale remains ambiguous

This study investigates the impacts of the timing of an extreme cyclone that occurred in August 2012 on the sea-ice volume evolution based on the Arctic Ice Ocean Prediction System (ArcIOPS). By applying a novel cyclone removal algorithm to the atmospheric forcing during 4–12 August 2012, we superimpose the derived cyclone component onto the atmospheric forcing one month later or earlier. This study finds that although the extreme cyclone leads to strong sea-ice volume loss in all runs, large divergence occurs in sea-ice melting mechanism in response to various timing of the cyclone. The extreme cyclone occurred in August, when enhanced ice volume loss is attributed to ice bottom melt primarily and ice surface melt secondarily. If the cyclone occurs one month earlier, ice surface melt dominates ice volume loss, and earlier appearance of open water within the ice zone initiates positive ice-albedo feedback, leading to a long lasting of the cyclone-induced impacts for approximately one month, and eventually a lower September ice volume. In contrast, if the cyclone occurs one month later, ice bottom melt entirely dominates ice volume loss, and the air-open water heat flux in the ice zone tends to offset ice volume loss.

Very few global‐scale ice volume estimates are available for mountain glaciers and ice caps, although such estimates are crucial for any attempts to project their contribution to sea level rise in the future. We present a statistical method for deriving regional and global ice volumes from regional glacier area distributions and volume area scaling using glacier area data from ∼123,000 glaciers from a recently extended World Glacier Inventory. We compute glacier volumes and their sea level equivalent (SLE) for 19 glacierized regions containing all mountain glaciers and ice caps on Earth. On the basis of total glacierized area of 741 × 103 ± 68 × 103 km2, we estimate a total ice volume of 241 × 103 ± 29 × 103 km3, corresponding to 0.60 ± 0.07 m SLE, of which 32% is due to glaciers in Greenland and Antarctica apart from the ice sheets. However, our estimate is sensitive to assumptions on volume area scaling coefficients and glacier area distributions in the regions that are poorly inventoried, i.e., Antarctica, North America, Greenland, and Patagonia. This emphasizes the need for more volume observations, especially of large glaciers and a more complete World Glacier Inventory in order to reduce uncertainties and to arrive at firmer volume estimates for all mountain glaciers and ice caps.

Variability and trends of Fram Strait sea ice area and volume exports are examined for the period of 1990–2010. Simulations from a high-resolution version of the MPIOM model (STORM project) reproduce area and volume export well when compared with NSIDC and ICESat satellite data and in-situ ice thickness observations. The fluxes derived from ice thickness and drift satellite products vary considerably, indicating a high uncertainty in these estimates which we mostly assign to the drift observations. The model captures the observed average seasonal cycles and interannual variability of ice export. The simulated mean annual sea ice area export is 860 × 103 km2 a− 1 (1990–2010), and the correlation with the NSIDC-based area fluxes is r = 0.67. The simulated mean annual volume export is 3.3 × 103 km3 a− 1 (1990–2010), close to the ICESat/ULS values, with a correlation of r = 0.58. The simulated monthly area export has a significant positive trend of + 10% per decade, explained by wind forcing. The major contribution to the robust trend in area export between June and September. Fram Strait ice volume export variability is mainly controlled by ice drift with a dominant role of the Transpolar Drift and, to a lesser extent thickness variability. The area export increase reflects increasing ice-drift speed, but is balanced with a reduced thickness over time when it comes to volume export, giving no significant trend in volume export. The spatial variability of ice drift indicates that the export influences a large area upstream in the Trans-Polar Drift stream, and that high volume export events lead to a thinner thickness there. The central Arctic is well connected drift-wise to the Fram Strait via the Transpolar Drift while for thickness, the region north of Greenland is dominated and controlled by the Fram Strait ice export.

Arctic sea ice export is important for the redistribution of freshwater and sea ice mass. Here, we use the sea ice thickness, sea ice velocity, and sea ice concentration (SIC) to estimate the exported sea ice volume through the Fram Strait from 2011 to 2018. We further analyse the contributions of the sea ice thickness, velocity and concentration to sea ice volume export. Then, the relationships between atmospheric circulation indices (Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Arctic Dipole (AD)) and the sea ice volume export are discussed. Finally, we analyse the impact of wind-driven oceanic circulation indices (Ekman transport (ET)) on the sea ice volume export. The sea ice volume export rapidly increases in winter and decreases in spring. The exported sea ice volume in winter is likely to exceed that in spring in the future. Among sea ice thickness, velocity and SIC, the greatest contribution to sea ice export comes from the ice velocity. The exported sea ice volume through the zonal gate of the Fram Strait (which contributes 97% to the total sea ice volume export of the Fram Strait) is much higher than that through the meridional gate (3%) because the sea ice flowing out of the zonal gate has the characteristics of a high thickness (mainly thicker than 1 m), a high velocity (mainly faster than 0.06 m/s) and a high concentration (mainly higher than 80%). The AD and ET explain 53.86% and 38.37% of the variation in sea ice volume export, respectively.

The well-documented decrease in the annual minimum Arctic sea ice extent over the past few decades is an alarming indicator of current climate change. However, much less is known about the thickness of the Arctic sea ice. Developing accurate forecasting models is critical to better predict its changes and monitor the impacts of global warming on the total Arctic sea ice volume (SIV). Significant improvements in forecasting performance are possible with the advances in signal processing and deep learning. Accordingly, here, we set out to utilize the recent advances in machine learning to develop non-physics-based techniques for forecasting the sea ice volume with low computational costs. In particular, this paper aims to provide a step-wise decision process required to develop a more accurate forecasting model over short- and mid-term horizons. This work integrates variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) for multi-input multi-output pan-Arctic SIV forecasting. Different experiments are conducted to identify the impact of several aspects, including multivariate inputs, signal decomposition, and deep learning, on forecasting performance. The empirical results indicate that (i) the proposed hybrid model is consistently effective in time-series processing and forecasting, with average improvements of up to 60% compared with the case of no decomposition and over 40% compared with other deep learning models in both forecasting horizons and seasons; (ii) the optimization of the VMD level is essential for optimal performance; and (iii) the use of the proposed technique with a divide-and-conquer strategy demonstrates superior forecasting performance.

The Fram Strait (FS) is the primary region of sea ice export from the Arctic Ocean and thus plays an important role in regulating the amount of sea ice and fresh water entering the North Atlantic seas. A 5 a (2011–2015) sea ice thickness record retrieved from CryoSat-2 observations is used to derive a sea ice volume flux via the FS. Over this period, a mean winter accumulative volume flux (WAVF) based on sea ice drift data derived from passivemicrowave measurements, which are provided by the National Snow and Ice Data Center (NSIDC) and the Institut Francais de Recherche pour d’Exploitation de la Mer (IFREMER), amounts to 1 029 km 3 (NSIDC) and 1 463 km 3 (IFREMER), respectively. For this period, a mean monthly volume flux (area flux) difference between the estimates derived from the NSIDC and IFREMER drift data is–62 km 3 per month (–18×10 6 km 2 per month). Analysis reveals that this negative bias is mainly attributable to faster IFREMER drift speeds in comparison with slower NSIDC drift data. NSIDC-based sea ice volume flux estimates are compared with the results from the University of Bremen (UB), and the two products agree relatively well with a mean monthly bias of (5.7±45.9) km 3 per month for the period from January 2011 to August 2013. IFREMER-based volume flux is also in good agreement with previous results of the 1990s. Compared with P1 (1990/1991–1993/1994) and P2 (2003/2004–2007/2008), the WAVF estimates indicate a decline of more than 600 km 3 in P3 (2011/2012–2014/2015). Over the three periods, the variability and the decline in the sea ice volume flux are mainly attributable to sea ice motion changes, and second to sea ice thickness changes, and the least to sea ice concentration variations.

Satellite altimeters have been used to monitor Arctic sea ice (ASI) thickness for several decades, but whether the different altimeter missions (such as radar and laser altimeters) are in agreement with each other and suitable for long-term research needs to be investigated. To analyze the spatiotemporal characteristics of ASI, continuous long-term first-year ice, and multi-year ice of ASI freeboard, thickness, and volume from 2002 to 2021 using the gridded nadirization method from Envisat, CryoSat-2, and ICESat-2, altimeter data are comprehensively constructed and assessed. The influences of sea surface temperature (SST) and sea surface wind field (SSW) on ASI are also discussed. The freeboard/thickness and extent/area of ASI all varied seasonally and reached their maximum and minimum in April and October, March and September, respectively. From 2002 to 2021, the freeboard, thickness, extent, and area of ASI all consistently showed downward trends, and sea ice volume decreased by 5437 km3/month. SST in the Arctic rose by 0.003 degrees C/month, and the sea ice changes lagged behind this temperature variation by one month between 2002 and 2021. The meridional winds blowing from the central Arctic region along the eastern coast of Greenland to the North Atlantic each month are consistent with changes in the freeboard and thickness of ASI. SST and SSW are two of the most critical factors driving sea ice changes. This study provides new data and technical support for monitoring ASI and exploring its response mechanisms to climate change.

Large-scale changes in Arctic sea ice thickness, volume and multiyear sea ice (MYI) coverage with available measurements from submarine sonars, satellite altimeters (ICESat and CryoSat-2), and satellite scatterometers are summarized. The submarine record spans the period between 1958 and 2000, the satellite altimeter records between 2003 and 2018, and the scatterometer records between 1999 and 2017. Regional changes in ice thickness (since 1958) and within the data release area of the Arctic Ocean, previously reported by Kwok and Rothrock (2009 Geophys. Res. Lett. 36 L15501), have been updated to include the 8 years of CryoSat-2 (CS-2) retrievals. Between the pre-1990 submarine period (1958-1976) and the CS-2 period (2011-2018) the average thickness near the end of the melt season, in six regions, decreased by 2.0 m or some 66% over six decades. Within the data release area (∼38% of the Arctic Ocean) of submarine ice draft, the thinning of ∼1.75 m in winter since 1980 (maximum thickness of 3.64 m in the regression analysis) has not changed significantly; the mean thickness over the CS-2 period is ∼2 m. The 15 year satellite record depicts losses in sea ice volume at 2870 km3/decade and 5130 km3/decade in winter (February-March) and fall (October-November), respectively: more moderate trends compared to the sharp decreases over the ICESat period, where the losses were weighted by record-setting melt in 2007. Over the scatterometer record (1999-2017), the Arctic has lost more than 2 × 106 km2 of MYI-a decrease of more than 50%; MYI now covers less than one-third of the Arctic Ocean. Independent MYI coverage and volume records co-vary in time, the MYI area anomalies explain ∼85% of the variance in the anomalies in Arctic sea ice volume. If losses of MYI continue, Arctic thickness/volume will be controlled by seasonal ice, suggesting that the thickness/volume trends will be more moderate (as seen here) but more sensitive to climate forcing.