The spatial correlation uses the derived average annual mass balances for each of the 1220 glaciers and provides results for either all of Tien Shan and Pamir or different regions divided according to two different classifications: HIMAP after according (1) to main mountain range names (Fig. ) and (2) to the mass balance variability found in . The latter is determined using a k-means clustering with three fixed classes, where points are classified by minimizing their squared distance to the iteratively determined cluster centers (; Fig. ). The k-means clustering is based on the standard deviation of the mass balance time series of and relies on an unsupervised classification that avoids a precise association of a class with, for example, a process. We arbitrarily chose a number of three clusters that would highlight the regions of high mass balance variability (hotspots) identified in . The division is performed to determine whether different dominant predictors exist for these regions and, in general, to assess the effect of arbitrarily subdividing glaciers into different groups.

We use either meteorological or morphological parameters as predictors. For meteorological predictors, we use the calculated average, standard deviation, and trend or tendency (derived slope from a temporal regression), each for the different seasons (spring, summer, autumn, winter) and the entire hydrological year (October–September) (Fig. ). As static morphological predictors, we include the variables latitude, longitude, slope over the glacier tongue, median elevation, aspect, and glacier area from the RGI and debris cover from . For aspect, we use the sine and cosine of the original values in the range between 0 and 360∘ to obtain consistent value ranges in zonal and meridional directions. We showcase, additionally, any possible relationship with surge activity using separate histograms and boxplots where we simplify the complex information on surge activity provided in as either (1) having experienced surge activity or (0) not during the 2000–2018 study period.

We run multiple linear regression analysis on the glacier mass balance time series per glacier to identify significant predictors over the time domain. Annual glacier mass balance time series represent the dependent variable; combinations of meteorological variables from individual reanalysis datasets represent the predictors. As this yields one result per glacier, we aggregate the results visually in the form of maps. We identify both significant variables and significant seasons to identify possible patterns and differences resulting from the use of a particular reanalysis or mass balance dataset.

A comparison of the different reanalysis datasets is shown for precipitation in Fig.  and for temperature in Fig. . Only CHELSA provides a spatial resolution to resolve intra-mountain-range variability. Differences in precipitation amount and different trends exist independently of the spatial resolution (Fig. ). However, for temperature, CHELSA is the temperature-conserving downscaled ERA5 product with matching trends and a correlation of 1 (Fig. i). For precipitation, CHELSA and ERA5 show deviating distribution patterns and trends, highlighting the nonlinear downscaling method (Fig. ). The use of the datasets in their original spatial resolution in consecutive analyses allow for an assessment of how downscaling affects the correlation with mass balance data.

Despite a high number of significant correlations between mass balances and meteorological variables (70 %–90 %), the variability between the different datasets is high, leaving uncertain the interpretation on dominant drivers for Tien Shan and Pamir (Fig. ). Obtained coefficients of determination (R2) are in the range of 5 % for all of Pamir and Tien Shan per variable and increase to up to 20 % for subregions (Fig. ). When including remote sensing SC in addition to the reanalysis meteorological variables (T and P), SC represents about half the significant correlations. Without SC, a similar number of significant correlations indicates that SC explains a similar portion of the variance in mass balance but slightly more significantly than the variables from the reanalysis. The R2 value remains low (Fig. ). No dominant season can be identified in the analysis for Tien Shan or Pamir. Variability in the meteorological variables within each season and for the hydrological year are equally important (Fig. b and d).

Of the tested morphological parameters, 60 % to 70 % show a significant correlation with mass balances for all of Tien Shan and Pamir (Fig. e and f), and no individual morphological variable stands out as the most dominant one (Fig. ). Each parameter is determined in 10 % of combinations as a significant predictor. Debris cover is not identified as an important variable in any case using mass balance datasets for either the short or long time period. This only changes in the regional division for some subregions (Fig. ). However, depending on the mass balance estimates and subregion, the identified morphological parameters can change entirely. The mass balance distribution indicates no significant difference between surge- and nonsurge-type glaciers (Fig. ). The small number of surge-type glaciers and the uneven distribution across the regions (Fig. ) prevent a conclusive result.

Considering the different HIMAP regions , the number of significant correlations between mass balance and meteorological variables varies strongly and depends on the mass balance dataset (Fig. ). Overall, the total number of identified important variables is similar in both mass balance estimates. However, strong differences are apparent regarding which variable and temporal aggregation (seasons) is dominant. Likewise, the choice of a different reanalysis dataset results in different variable and temporal-aggregation importance. For example, a large number of significant correlations are present for Western Pamir and Northern–Western Tien Shan for MBBarandunetal and for Central Tien Shan for MBHugonnetetal, whereas a medium number of significant correlations are present for Eastern and Central Tien Shan for MBBarandunetal and for Western Pamir and Pamir-Alay for MBHugonnetetal (Fig. ). For all other regions, the importance of meteorological variables is low and very variable from one dataset to another. For the majority of cases according to the HIMAP classification and for the entire region (Figs. , ), the number of significant correlations is somewhat higher for the more highly spatially resolved CHELSA time series than for ERA5, despite CHELSA being based on ERA5 data as input. However, using the division according to the k-means clusters, this effect is not perceived (Fig. ).

Snow cover increases the total number of significant correlations only for Central Tien Shan for MBBarandunetal and for Eastern Tien Shan, Pamir-Alay, and Western and Eastern Pamir for MBHugonnetetal, despite contributing to at least half the significant combinations for most regions. For all other regions, T and P provide more significant correlations when SC is not considered. Combinations with SC barely show significant correlations in Eastern Pamir and Dzhungarsky Alatau (Fig. ). Thus, incorporation of SC does not notably increase the number of significant correlations overall. Instead, a shift of important variables from P and T to SC occurs. This distinct shift from P and T to SC suggests that potential collinearity does not bias the general outcome.

We found clearly more significant combinations including P than those including T for Western Pamir and Pamir-Alay. T seems more significant for Eastern and Northern–Western Tien Shan and, to some degree, for Central Tien Shan. For Dzhungarsky Alatau, the results vary strongly between the different datasets, preventing a conclusion on the dominance of one of the meteorological variables. For Western Pamir and Eastern Tien Shan, winter and spring changes contribute to most of the significant combinations. The autumn season for Pamir-Alay and the summer and autumn seasons for Central and Northern–Western Tien Shan show the most significant correlations. For Eastern Pamir and Dzhungarsky Alatau, the different seasons contribute nearly equally to the number of significant combinations.

Besides the importance of the meteorological variables, the morphological-parameter importance is relatively high for Western Pamir (approx. 50 %; Fig. ). However, no dominant morphological parameter could be identified, and variability across the different HIMAP regions and for the different mass balance estimates is high (Fig. ). The only region in which both morphological and meteorological parameters show a high frequency of significant correlations is Western Pamir. In contrast, for Dzhungarsky Alatau and Eastern Pamir, none of these two categories of parameters show a high number of significant correlations with any of the mass balance estimates.

In contrast to the results for the regional HIMAP classification, a high number of significant correlations is always present within the k-means-derived hotspot regions of different mass balance variabilities (Fig. a and b). We found significant correlations for over 80 % of meteorological variables in cluster 1, slightly fewer in cluster 3 (60 %–80 %), and the lowest number, with high variability depending on the meteorological dataset used, for cluster 2 (40 %–70 %). Including SC did not increase the overall number of significant combinations for any of the three clusters, but this is the most important (relative highest number of correlations) variable for clusters 1 and 3. For cluster 1 (mainly positive and more balanced hotspot and low mass balance variability in MBBarandunetal), P seems slightly more important than T; for cluster 2 (average mass balance with low variability, mainly along western/northwestern margins) T is dominant, and SC shows the lowest number of significant correlations compared with that of the other clusters; for cluster 3 (negative hotspot and high mass balance variability in MBBarandunetal), T is slightly more dominant (Fig. ).

For cluster 1, no clear dominance of either season is visible (Fig. c and d). In contrast, for clusters 2 and 3, some dominant seasons are apparent although dependent on the mass balance dataset. Generally, a more even distribution in seasonal importance is present for MBHugonnetetal than for MBBarandunetal. The highest variability of dominant seasons was found for cluster 2 based on the reanalysis dataset used, with especially strong variability in the importance of spring and winter. Regarding the two mass balance estimates, MBBarandunetal shows dominance in summer and autumn, while MBHugonnetetal shows dominance in winter and spring. The morphological variables show fewer significant correlations than the meteorological variables, especially for MBBarandunetal in clusters 2 and 3 (Fig. ).

The explanatory power of the meteorological variables for the glacier-wide mass balance time series per individual glacier is between 30 % and 50 % (Fig. ). However, similar to the spatial analysis, regional and dataset-dependent differences are apparent.

The importance of the variables depends strongly on the dataset used, and our analyses led to opposing results using the different products. This not only concerns the different reanalysis products but also the different mass balance time series. As an example, for Pamir-Alay, SC is the dominant variable for MBHugonnetetal, whereas T and P are the more important ones for MBBarandunetal. A similar discrepancy was found for the different reanalysis datasets (Fig. ); for example, for MBHugonnetetal, the central and southern parts of western Pamir are dominated by T using the HAR dataset, by SC using ERA5, and by P using CHELSA. This discrepancy is even clearer when SC is excluded from the analysis and only T and P are used as predictors for the mass balance time series (Fig. ).

Similarly, for the seasonal importance, results vary largely for the different glaciers depending on the dataset used and provide a more detailed view than that of the spatial analysis (Fig. ). No consistency was found either for the same mass balance time series with different reanalysis products or for different mass balance time series with the same reanalysis product. When including SC, the pattern of variable importance becomes slightly more consistent in regions where SC replaces other variables as the most important variable (Fig. ) without any apparent change in explained variance (Fig. ).

CHELSA is a processed and downscaled version of the ERA5 dataset. When using these two products, differences appear in the correlation analyses due to different spatial resolutions in the predictor variables; however, these differences only become striking when using the regional subdivision (Fig. ). This might respond to a low number of data points with high mass balance variability within areas of encompassing meteorological grid cells, while the mass balance variability between the different encompassing grid cells in the regional subdivision is rather low. The nonlinearly downscaled precipitation of CHELSA (Fig. ) compared with the conserving values for T (Fig. ) can explain the higher number of significant correlations of this product compared with ERA5. As we use two predictor variables, the original P fields of CHELSA can change the significance of T. In the temporal analysis, the effect is even clearer, leading to partly opposing results (Figs.  and ). We do not answer here how exactly any downscaling method can affect results, but we show that advanced downscaling methods (CHELSA's precipitation) can severely impact driver interpretation. Different outcomes can be expected depending on the downscaling technique used, adding subjectivity to the results.

Our results reveal a high number of significant correlations between T, P, and mass balance for the entire Pamir and Tien Shan region (Fig. ), largely independent of the mass balance estimate or reanalysis product. The relative number of significant correlations remains similar when introducing SC, suggesting that the meteorological parameters from the reanalysis and SC relate similarly to the spatial variability in glacier mass balances.

When dividing glaciers regionally, the number of significant correlations between T, P, SC, and mass balance decreases, and the identified variables are shown to depend strongly on the datasets used (Figs.  and ). The low number of significant correlations for certain subregions can respond to (1) data availability, (2) data quality and scale issues, and (3) process representation.

Due to the lower number of glaciers with mass balance time series available, especially for Eastern Pamir and Dzhungarsky Alatau, the robustness of the statistical analysis is reduced, making it difficult to find a significant correlation with the meteorological variables.

Our analysis reflects all of the above issues. Glaciers in cluster 2 (subcontinental climate regime) are more heterogeneously distributed spatially (Fig. ), and this cluster shows the lowest number of significant correlations. In combination with the high variability within the datasets, our results suggest that glacier mass balance variability does not result from the investigated meteorological drivers for this cluster, suggesting that a similar mass balance variability is a response to different drivers within this cluster. Conversely, glaciers in clusters 1 (continental climate regime) and 3 (hotspots of mass balance variability) are much more aggregated spatially, and the investigated meteorological and morphological parameters better represent the subregional mass balance variability. At this point especially precipitation estimates are imprecise in general , as well as at the glacier scale as a result of, e.g., coarse resolution or insufficiently represented localized precipitation events such as orographic precipitation effects . What is responsible for the high variability within the data-scarce region of cluster 2 remains unclear. An interesting finding is the lower number of correlations when including SC for both mass balance estimates. This is especially pronounced for cluster 2 (Fig. ), where a low fraction of variable combinations identifies SC in combination with MBBarandunetal. This can indicate that a crucial meteorological component is missing in our analysis and that T and P estimates are a surrogate for a process not related to snow dynamics. For these cases, glacier response might be more related to radiation, physical snow properties (e.g., albedo, grain size), or the amount of snow, which in total are better represented by simply using T and P than with the qualitative information of SC. In contrast, a similar number of correlations would indicate that T and P explain processes similarly to SC.

Our results indicate a tendency for stronger summer and autumn influence along the northwestern margin and, to some extent, for the negative hotspot that could be linked to either summer snowfall events or changing melt rates due to changes in air temperature. This contrasts the importance of autumn to spring for the positive mass balance anomalies in Western Pamir and Central Tien Shan, which can relate to changing snow cover dynamics and/or changes in solid–liquid precipitation ratios, supported by the high number of significant correlations including P. However, we have not identified a strong seasonal importance of the meteorological variables in our spatial analysis (Figs.  and ).

Our results show a heterogeneous picture of dominant variables and seasons from 2000 to 2014 (2018), strongly dependent on the choice of datasets. An interpretation based on an individual dataset can contradict a different interpretation based on another set of data, both of them well supported by the literature. Here we show two somewhat contradicting but plausible interpretations of meteorological drivers for glacier mass balance changes based on the results of our temporal and spatial analysis to highlight the persistent difficulty to shed light onto the glacier–climate interaction in a heterogeneous and data-sparse region such as Central Asia.

The resulting importance of the seasons and meteorological variables from the temporal and spatial analyses is strongly dependent on the dataset used, sometimes suggesting even a contradicting relationship with glacier response. Clear nonclimatic drivers cannot be identified. Derived interpretations (from Analysis I and Analysis II) align with other reported findings in these regions, as shown in Sect. , rendering the interpretation and analysis even more difficult. Available time series fall short of robust evidence in the correlation analysis due to high meteorological and mass balance variability compared with rather slight trends and tendencies in these time series. Using different meteorological and mass balance estimates, our study highlights in all cases a highly complex glacier response to climate variability and change. The current lack of sufficiently detailed and qualitatively satisfying data prevents elucidating the complex relationship between glacier mass balance and climatic drivers in Tien Shan and Pamir.

A lack of regional-wide systematic cryospheric and atmospheric monitoring limits the validation of the available datasets for scientific applications, resulting in an elevated uncertainty for mass balance modeling and interpretation on the underlying processes of glacier response to climate change. We cannot rate the different products. Cryological, hydrological, and meteorological monitoring at high elevation throughout the different subregions of Tien Shan and Pamir needs to improve for a better understanding of glacier response to climate change. In several subregions, no glaciers are systematically monitored , in situ snow monitoring is not established, and hydrological monitoring is very limited . Meteorological measurements are underrepresented at high elevation and, for certain subregions, completely lacking . Most existing data are inaccessible. The needed long-term systematic monitoring of the different components of the water cycle often lacks financial support, know-how, and personpower . When using only one specific dataset, differences in reanalysis products and elevated uncertainties tied to the available annual mass balance time series for Tien Shan and Pamir underpin the ambiguity in the interpretation of the results of a correlation analysis. As highlighted by the two scenarios presented in Sect. , any derived understanding of glacier–climate interactions depends strongly on the dataset used. When a thorough quality assessment of the different reanalysis products for further application cannot be assured, the limitations of these datasets cascade into an increase of uncertainties. This especially affects the modeling of future glacier response, hydrological modeling, and the equifinality problem related to too many unknowns in the cryohydrological cycle e.g., or reconstruction of glacier mass change without a good calibration dataset. Traditionally, many studies have been based on a single reanalysis dataset, used either for bias correction or directly as model input, and direct calibration data are generally limited to a few individual locations, insufficient for regional applications. Remote sensing can partly bridge the gap in observational data, and progress has been made to increase temporal resolution of geodetic mass balance estimates . Furthermore, integration of different and sometimes unconventional datasets with information on the atmospheric conditions, the earth surface energy balance, glacier response, and other water storage changes is valuable for assessing and possibly quantifying uncertainties related to the presented datasets and, eventually, rating their quality. Modern downscaling techniques such as those provided in facilitate the inclusion of small-scale topographic effects. Standardizing sophisticated downscaling methods can improve the spatial representations of the reanalysis datasets. Despite potential improvements (remote sensing, ; integration of unconventional datasets, ; and modern downscaling technics, ), we recommend not relying on a single product for either correlation analysis or cryohydrological modeling.