Stable water isotope signals in inland Antarctic ice cores have provided wealth of information about past climates. This study investigated atmospheric circulation processes that influence precipitation isotopes in inland Antarctica associated with atmospheric circulations in the southern mid-latitudes during the Last Glacial Maximum (LGM, ~21 000 year ago). We simulated this climate period using circulation model (MIROC5-iso) forced with different sea surface boundary conditions. Our results showed a steepened meridional sea surface temperature gradient in the southern mid-latitudes associated with a strengthening of the southern westerlies. This change in the atmospheric circulation enhanced the intrusion of warm and humid air from low latitudes that contributes to precipitation events, inducing heavy isotope precipitation inland East Antarctica. Our results suggest that the representation of past southern westerlies can be constrained using water isotopic signals in Antarctic ice cores.

Stable water isotopes in inland Antarctic ice cores are powerful paleoclimate proxies; however, their relationship with dynamical atmospheric circulations remains controversial. Using a water isotope climate model (MIROC5-iso), we assessed the influence of the Last Glacial Maximum (LGM; ~21,000 years ago) sea surface temperatures (SST) and sea ice (SIC) on Antarctic precipitation isotopes (δ18Op) through atmospheric circulation. The results revealed that the synoptic circulation mostly maintained southward moisture transport, reaching inland Antarctica. The steepened meridional SST gradient in the mid-latitudes increased δ18Op in inland Antarctica by enhancing the baroclinic instability and synoptic moisture transport. In contrast, enhanced SIC reduced the atmospheric humidity around Antarctica and lowered δ18Op through extensive surface cooling and transport from the ocean. These findings elucidate the isotopic proxies and enable us to constrain the southern hemisphere atmospheric circulation, including the westerlies, using ice cores during past climates, including the LGM.

With urbanization, anthropogenic water vapor emissions have become a significant component of the urban atmosphere. Fossil fuel combustion-derived vapor (CDV) is a primary source of these emissions. Owing to the notably low CDV d-excess, stable hydrogen and oxygen isotopes are promising for distinguishing CDV from natural sources. Considering the limitations of in situ observations, this study aims to explore the feasibility of using IsoRSM, an isotopically enabled regional atmospheric model, to simulate CDV emissions in urban areas in winter. Two experiments were conducted: one in Salt Lake City in January 2017 and another in Beijing in January 2007. The simulation results showed that the CDV addition significantly reduced the water vapor d-excess, particularly when the boundary layer was stable. The simulation with CDV emissions aligned better with the time series of in situ observations in Salt Lake City. The modification led to a more pronounced positive correlation between vapor d-excess and specific humidity, which was similar to the observation of Salt Lake City. The CDV inclusion significantly increased the vapor d-excess variability with varying wind directions in both sites. However, in Beijing, the underestimation of d-excess variation from natural sources caused a bigger discrepancy between the observed and simulated d-excess and CDV fraction. Thus, though there were still biases, the inclusion of CDV could improve the accuracy of isotopic simulation in the urban regions where CDV was one of the controlling factors of vapor d-excess.

In climate reconstructions by data assimilation, the sensitivities to both proxies and prior estimates need to be taken into account because models are uncertain and proxies are limited spatiotemporally. This study examines these sensitivities using multiple climate model simulations and different combinations of proxies (corals, ice cores, and tree-ring cellulose). Experiments were conducted based on an offline data assimilation approach. These experiments show annual variations in the global distribution of surface air temperature and precipitation range from 850 to 2000. The results indicate that standard deviations of surface air temperature and precipitation amount during the entire period differ by up to 50% due to prior estimates. Experiments with different types of proxies show that the El Niño-like distribution of positive anomalies in the central to eastern tropical Pacific can be reproduced adequately in experiments with corals, but not in experiments without corals. The correlation coefficient of the NINO.3 index from 1971 to 2000 between experiments with corals and the Japanese 55-year Reanalysis (JRA-55) were 0.79 at maximum, while the correlation coefficient between experiments without corals and JRA-55 were 0.20 at maximum.

Water isotopes measured in Antarctic ice cores enable reconstruction at the first order of the past temperature variations. However, the seasonality of the precipitation and episodic events, including synoptic-scale disturbances, influence the isotopic signals recorded in ice cores. In this study, we adopted an isotope-enabled atmospheric general circulation model from 1981 to 2010 to investigate variations in climatic factors in δ18O of precipitation (δ18Op) at Dome Fuji, East Antarctica. The Southern Annular Mode (SAM), the primary mode of atmospheric circulation in the southern mid-high latitudes, significantly contributes to the isotope signals. Positive δ18Op anomalies, especially in the austral winter, are linked to the negative polarity of the SAM, which weakens westerly winds and increases the southward inflow of water vapor flux. Daily variations in temperature and δ18Op in Dome Fuji are significantly small in the austral summer, and their contribution to the annual signals is limited. The isotope signals driven by the SAM are a locational feature of Dome Fuji, related to the asymmetric component of the large-scale atmospheric pattern.

Old descriptive diaries are important sources of daily weather conditions before modern instrumental measurements became available. A previous study demonstrated the potential of reconstructing historical weather at high temporal resolution by assimilating cloud cover converted from descriptive diaries. However, cloud cover often exhibits a non-Gaussian distribution, which violates a basic assumption of most data assimilation schemes. In this study, we applied a Gaussian transformation (GT) approach for cloud cover data assimilation and conducted observing system simulation experiments (OSSEs) using 15 randomly selected observation points over Japan. We performed experiments to assimilate cloud cover with large observational errors using the Global Spectral Model (GSM) and a local ensemble transform Kalman filter (LETKF). Without GT, temperature and zonal and meridional wind exhibited deterioration compared to the experiment assimilating no observations. By contrast, the 2-month root mean square error (RMSE) of zonal wind, meridional wind, temperature, and specific humidity at mid-troposphere were improved by 8.7%, 5.1%, 4.2%, and 1.4%, respectively, through GT. Among two-dimensional variables, the 2-month RMSE of total cloud cover, surface pressure, rainfall, and downward solar radiation were improved by 2.2%, 5.2%, 27.6%, and 4.3%, respectively. We further demonstrated that the effect of GT was more pronounced on clear days. Our results show the potential of GT in high-resolution historical weather reconstruction using old descriptive diaries.

Various bias correction methods have recently been proposed using machine learning techniques. Generally, machine learning methods are fairly complicated, and it is extremely difficult to explain how machine learning corrects model biases. Accordingly, researchers perpetually seek to apply machine learning methods to diverse cases and to determine whether these methods are reliable. Here, we developed a machine learning method using simple input data by assuming a relation between observed and simulated precipitation corresponding to weather conditions. This simple method can find the optimal relation without employing dimension reduction and can facilitate the comprehension of precipitation characteristics. According to a validation experiment, this simple method can correct the precipitation frequency corresponding to the orography and estimate the local precipitation distribution characteristics, resulting in values similar to the observed data even when data are forecasted more than 24 hours from the initial time.

Snow algae are found from spring to summer on snowfields and glaciers throughout the world. Their blooming darkens snow surfaces, reducing snow surface albedo and accelerating melting. Uncertainties remain, however, regarding the blooming season and global distribution of these algae. To reproduce snow algal bloom temporal and spatial variability, we improved an existing snow algae model using a land surface model calibrated with a global atmospheric reanalysis dataset. Snowfall and daylight length data for selected model locations were also incorporated. To evaluate its performance, we used in situ observational data from 15 polar to alpine area sites. The improvements made in this study allowed the reconstruction of detailed snow algal blooming reports from various locations worldwide, and the results suggested that the major factors affecting the appearance of snow algal blooming were the snow melting period duration and algal growth interruption by new snow cover. We then incorporated the updated snow algae model into a land surface model and performed a global simulation. In this case, our simulation suggested that red snow could appear on snowfields during the melting season but only in the absence of frequent new snow falls, and if the snow cover persists long enough to allow prolonged algal growth.

Data assimilation (DA) has been applied to estimate the time-mean state such as annual mean surface temperature for paleoclimate reconstruction. There are two types of DA for this purpose: online-DA and offline-DA. The online-DA estimates the time-mean states and the initial conditions for the next DA cycles while the offline-DA only estimates the former. If there is sufficiently long predictability in the system of interest compared to the temporal resolution of the observations, online-DA is expected to outperform offline-DA by utilizing information in the initial conditions. However, previous studies failed to show the superiority of online-DA when time-averaged observations are assimilated, and the reason has not been investigated thoroughly. This study compares online-DA and offline-DA and investigates the relation to the predictability using an intermediate complexity general circulation model with perfect-model observing system simulation experiments. The result shows that the online-DA outperforms offline-DA when the length of predictability is longer than the averaging time of the observations. We also found that the longer the predictability, the more skillful the online-DA. Here, the ocean plays a crucial role in extending predictability, which helps online-DA to outperform offline-DA. Interestingly, the observations of near-surface air temperature over land are found to be highly valuable to update the ocean variables in the analysis steps, suggesting the importance to use cross-domain covariance information between the atmosphere and the ocean when online-DA is applied to reconstruct paleoclimate.

Stable isotopes in precipitation and vapor are a powerful tool for tracing the origin of moisture and mixing processes. This paper discusses time and space variation of δ18O in precipitation and controlling features over upper Blue Nile Basin using data from GNIP, observed data in 2014 and simulated data by AGCM. IsoGSM simulation in precipitation was verified with observation. The δ18O variation shows clear seasonality with the lowest 18O values in August and dry season, and enriched 18O in spring, June and September. Spring sample is enriched compared to summer, and assumed to be related with moisture sources. More enriched isotopes in spring and lower d-excess could be related to the source of air masses in short travel path from North Indian Ocean, Mediterranean and Red sea while summer rain is depleted with larger d-excess could be related to longer travel path of moisture from south Indian Ocean with mixing of potential evaporated moisture from open surface and transpired moisture from Congo vegetation and also from Gulf of Guinea. The isotopic statistics of three stations shows maximum, minimum and average value of (8.23‰, -11.73‰, 0.04‰) in Addis Ababa, (5.26‰, -12.74‰, and -2.52‰) in Entoto Hill and (4.08‰, -9.65‰, 2.41‰) in Debremarkos respectively. The δ2H- δ18O relationships, monthly weighted d-excess variation in the Basin revealed the temporal variation of δ18O in precipitation is essentially shaped by the source of the moisture and spatial differences is due to Rayleigh rainout effect along the moisture trajectory. The source of moisture is primarily controlled by the north south movement of ITCZ within the Basin. The study recommends the use of model simulated δ18O as good alternative for hydrological and hydrologeological investigations when needed.