Climatology D.s. Lal Pdf Free Download

I have an SST climatology(lat,lon,time) where the time dimension is 12 months. I would like to copy the climatology so I have a dataset of say 100 years in which the SST data is simply the climatology repeating every year.

By visual inspection of Fig. 12.7a, there is nontrivial clustering in both temperature and precipitation among models belonging to the same family. A formal hierarchical clustering of the baseline and future climatology (not shown) confirms the tendency of models within the same family to cluster with respect to simulated regional precipitation and temperature averages. The degree to which clustering occurs within each family depends on the model family being considered (GISS and HadGEM cluster well, IPSL/CMCC less so). There is a tendency for models with very similar atmospheric structures to cluster, even if other components are different.

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Staff used a variety of tests to assess data quality. The first step involved comparing stations with a gridded climatology and plotting the stations for visual inspection. Both of these processes uncovered mislocated stations and the digitized formerly uncovered stations 6 months out of phase. Additionally, each time series was tested for significant discontinuities using the Cumulative Sum test (which looks for changes in the mean) and an analogous test that looks for changes in the variance or scale. Evaluation of each time series for runs of three or more months of the same nonzero value. Finally, scientists evaluated each individual precipitation total to determine if it was an outlier in space and/or time using a variety of nonparametric statistics.

If I use dask.array.ones to mimic the original netCDF collection with same chunks, and the same cluster settings (4 processes, 8GB total), flox with map-reduce finishes in just over a minute. This fits my intuition (phew!). No rechunking should be necessary to do an hourly climatology here.

In this study, we provide a perspective on dynamical downscaling that includes a comprehensive view of multiple downscaling methods and a strategy for achieving better assessment of future regional climates. A regional climate simulation is generally driven by a large-scale atmospheric state obtained by a global climate simulation. We conceptualize the large-scale state based on reconstruction by combining decomposed components of the states, such as climatology and perturbation, in different global simulations. The conceptualization provides a comprehensive view of the downscaling methods of previous studies. We propose a strategy for downscaling regional climate studies based on the concept of covering a wider range of possibilities of large-scale states to account for the uncertainty in global future predictions due to model errors. Furthermore, it also extracts the individual influences of the decomposed components on regional climate change, resulting in better understanding of the cause of the change. We demonstrate a downscaling experiment to highlight the importance of the simultaneous consideration of the individual influences of climatology and perturbation.

For DS, one of the most important issues is which large-scale state is used to drive the DS because the statistical characteristics of the downscaled state are significantly affected by the large-scale state. There are several DS methods in terms of derivation of the large-scale state. One such method is direct DS (DDS), where the large-scale state obtained by a general circulation model or global climate model (GCM) is directly used. It has been pointed out that the climate bias in large-scale states has a significant impact on the reproducibility of the downscaled state (e.g., Kato et al. 2001; Wang et al. 2004). Another DS method is unbiased downscaling (Unbiased-DS), where the large-scale state obtained from a GCM experiment is used, as in DDS; however, the climate bias in the state from the GCM is removed. The climate bias in a GCM is often estimated as the difference between the climatology obtained by a GCM experiment under the current climate conditions and that obtained through reanalysis (e.g., Misra and Kanamitsu 2004; Holland et al. 2010; Done et al. 2015). Here, the model bias under future climate conditions is assumed to be identical to that under the present climate condition, although the bias may change under different climates (Bellprat et al. 2013). There is also an attempt to reduce the perturbation bias (e.g., Xu and Yang 2012; Jin et al. 2011). Another DS method is pseudo climate change downscaling (Pseudo-Clim-DS), which is also used to reduce the model climate bias, where the large-scale state is artificially constructed by adding a certain climate difference to the reanalysis state. An ideally constructed climate difference was used in Schr et al. (1996). The difference between the future projection and the present run in a GCM is also used as the climate difference (e.g., Wu and Lynch 2000; Kimura and Kitoh 2007; Sato et al. 2007; Cook and Vizy 2008; Kawase et al. 2009; Patricola and Cook 2010; Rasmussen et al. 2011), i.e., the climate bias in the model is treated in the same way as in Unbiased-DS. Wakazuki and Rasmussen (2015) constructed the climate difference statistically using multiple GCM simulations. Pseudo-Clim-DS attempts to reduce the perturbation bias using the perturbation value from the reanalysis. The validity of using the present perturbation in future DS projections was investigated by applying Pseudo-Clim-DS to a past climate in Kawase et al. (2008) and to an assumed true climate (ATC), i.e., the so-called perfect model experiment, in Yoshikane et al. (2012). Another advantage of the Pseudo-Clim-DS is that it allows us to investigate the influence of change in a target component on regional climate (e.g., Rowell and Jones 2006; Adachi et al. 2012; Krner et al. 2016).

For future regional projections by DS, GCM results are utilized to obtain the large-scale state driving the DS. However, in GCMs, there are some inevitable limitations caused by model errors in terms of the obtained large-scale states for DS. These limitations result in errors or uncertainties in the estimation of future regional change by DS, as well as uncertainties in future external conditions, such as in a scenario of greenhouse gas emission. A multi-model ensemble is a good strategy to evaluate the uncertainty, and much valuable knowledge has been obtained from ensemble experiments (e.g., van der Linden and Mitchell 2009; Means et al. 2013; Evans et al. 2014). However, uncertainties due to the limitations in individual models still have to be taken into account in order to achieve more reliable evaluations. Therefore, it is very important to consider the large-scale state obtained by GCM simulations in order to interpret the DS results. In this study, we consider the limitation from a possible error in the relationship between climatology and perturbations in GCM simulations through a consideration of the phase space of a large-scale state. On the basis of this consideration, we provide a comprehensive view of DS methods. We also propose a strategy for regional climate simulations with the goal of achieving better estimations and understanding of future changes in regional climate.

The large-scale state can be divided into multiple components. As one of the simplest decompositions, we consider climatology and the deviation from it (referred to as a perturbation) as two components.

Schematic diagram of the decomposition of the large-scale state in a climatology-perturbation phase space. Horizontal and vertical axes represent climatology and perturbation, respectively. For simplicity, climatology and perturbation are represented as scalar values. Cross marks show two states of a system. Stars represent the possible large-scale states driving the downscaling simulations. The yellow line shows the manifold corresponding to the relationship between climatology and perturbation

The statistical characteristics of the climatology and perturbation are not independent of each other, since they are determined through their interactions with one another. Roughly speaking, they have a one-to-one correspondence, although there can be some ambiguities due to the possible existence of multi-equilibrium states. Here, we consider the statistical characteristics derived from the probability distribution function of possible states under the same climate condition, e.g., ensemble members in a GCM experiment; the characteristics of possible perturbations under a climate do not depend on individual realizations. The one-to-one correspondence means that states can exist only in a manifold in the entire phase space because they are constrained by their relationship, as indicated by the yellow lines in Fig. 1.

None of the numerical models are perfect, and the representation of interaction among components depends on the GCMs. That is, their relationship in GCMs may be distorted from that in nature. For example, an aqua-planet intermodel comparison shows a variety of relationships: there exist significant differences in precipitation systems compared to the differences in mean flow (Blackburn et al. 2013). The former is part of the perturbation, and the latter is part of the climatology. Because of the distorted relationship in a GCM, the states in a numerical simulation may exist in a different manifold from that of nature, as shown in Fig. 2. One remarkable point here is that there is a significant probability of a real atmospheric state existing outside the region covered by numerical simulations in the phase space. DS driven only by the large-scale state in a distorted manifold can lead to an incorrect estimation of a regional climate.

Schematic diagram of the large-scale state driving the downscaling simulations. Cross marks show the states realized by reanalysis and GCM simulations. Stars represent the possible large-scale states driving the downscaling simulations. The orange shaded area represents the range of uncertainty in the estimation or existence probability distribution of the future state. The yellow line shows the manifold corresponding to the relationship between climatology and perturbation ff782bc1db