We used a simple local example in a preliminary attempt to assess the challenge of discrepancies between RPs. We compared four RPs against observations from a weather station near a long-term agricultural trial in Uppsala, Sweden. Analysis of daily and monthly absolute error for rainfall and temperature showed that the four RPs provided a fair representation of local climate relative to observations (Figs. 1 to 3). Monthly precipitation amount (Fig. 1) was depicted accurately, with concordance correlation coefficient (CCC) ranging from 0.79 to 0.97. Daily precipitation values showed weaker agreement with observations, but were acceptable (CCC ≥ 0.6). Observed and RP-derived minimum (Tmin) and maximum (Tmax) temperature showed stronger correlations, with CCC > 0.95 for monthly error and > 0.90 for daily error. The slightly lower agreement seen for precipitation is consistent with the inherent characteristics of precipitation as a more stochastic parameter, with higher variation in time and space than temperature. This preliminary analysis showed good representativeness of RPs in depicting the general climate at the local field site.
Our next task was to capture the incidence, distribution, and accumulated duration of wet and dry days during the cropping period to assess the potential impact on yield. The four RPs were used to depict the number of rainy days (> 1 mm) in 10 cropping seasons at the study site (Fig. 2). All RPs consistently overestimated the number of rainy days, by 10–35% compared with observed data. MESAN showed best agreement (CCC = 0.71) with observed patterns, but agreement decreased substantially for the other RPs (CCC = 0.19–0.25).
Another important criterion is how RPs distribute wet and dry days over the growing season, and generate wet and dry spells. Based on the temporal distribution and relationship between number and length of spells for the four RPS, all underestimated dry spells and overestimated wet spells (Fig. 3). For instance, of 25 observed dry spells lasting 10–14 days, MESAN captured 22, NASApower and AgERA5 15, and HARMONIE 14. Of 18 extreme dry spells lasting > 15 days, MESAN, NASApower, AgERA5, and HARMONIE captured 15, 6, 8, and 6, respectively. On the other hand, only five wet spells lasting 5–9 days were observed, but MESAN, NASApower, AgERA5, and HARMONIE returned 13, 42, 29, and 37 respectively. This overestimation of wet spells is consistent with the excess number of rainy days identified in Fig. 2.
The data in Figs. 2 and 3 represent only one grid cell of each RP, but this local agro-climatic approach is necessary to investigate climate impacts on crop yield (quantity and quality). Our final task was to estimate, at larger scale, the difference induced by choice of RP. Scaling up the analysis to territory level using classical weather station is generally impossible, as lack of spatialized observed data is the reason for using RPs. We assessed the difference between RPs by comparing them for the study region, i.e., the agriculture-dominated region of southern Sweden. HARMONIE and MESAN, which diverged most strongly in the dry/wet spell analysis in Fig. 3, were compared (Fig. 4).
For southern Sweden 1990–2000, a difference between the two RPs of less than five events were observed in 38% of cells for dry spells lasting 5–9 days. This increased to 58% of cells for dry spells lasting 10–14 days, but declined to 32% for spells > 15 days. At some locations, HARMONIE returned only one dry spell > 15 days over the decade, while MESAN returned 11 (Fig. 4).
Reanalysis products offer a useful solution to the problem of lack of observed weather data and are very often used in crop modeling studies1,3,9,20. However, we showed clearly that the frequency and intensity of dry and wet spells returned can differ widely between RPs. When RP data are used in agricultural models, this divergence in representation of dry and wet spells can generate substantial differences in impact analysis of crop yields and quality. There are also implications for strategies and investments in agricultural water management (drainage and irrigation), as system design, precision, and cost-benefit must be conducted at high spatial and temporal resolution in order to be meaningful for local farmers and beneficiaries.
The bias of dry and wet day sequences highlighted in this manuscript is coherent with the main objective of RPs which is primarily to characterize the climate at large scale. Typical approaches used to validate them are usually considering the spatial and temporal large scale factors15–18. Their usage for agricultural modeling is then adapted to evaluate the interaction between crop production and climate variable at national or regional scale if related to temperature (e.g. 21,22). However, as illustrated here, those products should be carefully used for assessing climate impacts – especially those linked to precipitation – on more reduced scale.
A noteworthy finding here was for instance the underestimation of long dry spells (> 10 days), i.e., of the risk of drought, and resulting yield and food security implications at local or even regional level. Representation of meteorological events resulting in dry and wet spells, which is not generally considered when evaluating RP quality, is a future challenge for agro-climatic research.
Of the four RPs investigated, MESAN (available until 2013) best depicted dry and wet days and spells. MESAN was developed over a more limited area (northern Europe) than the other RPs (European or global scale), which could explain its better representation of agro-climatic parameters at the Swedish field site. Our findings indicate that agro-hydrologists and agro-meteorologists need to exercise caution when choosing climate RPs for agricultural research. The scientific community should work to improve representations of important agro-climatic features, in particular the distribution of wet and dry spells, in evaluations of soil moisture and yield responses in agro-climatic investigations.
This comparison of RPs was conducted in a region with a dense observation data network, on which the RPs are based. Divergence between available RPs may be even stronger in poorly monitored regions, such as sub-Saharan Africa. The issue of accurate representation of dry and wet spells may also arise in results generated by climate models, which are widely used to project food production over the next century.