bring back new functions lost after merging develop

climada/hazard/trop_cyclone/trop_cyclone.py

@@ -37,7 +37,7 @@
 
 from climada.hazard.base import Hazard
 from climada.hazard.tc_tracks import TCTracks
-from climada.hazard.tc_clim_change import get_knutson_criterion, calc_scale_knutson
+from climada.hazard.tc_clim_change import get_knutson_scaling_factor
 from climada.hazard.centroids.centr import Centroids
 import climada.util.constants as u_const
 import climada.util.coordinates as u_coord
@@ -366,46 +366,99 @@ def from_tracks(
 
     def apply_climate_scenario_knu(
         self,
-        ref_year: int = 2050,
-        rcp_scenario: int = 45
+        percentile: str='50',
+        scenario: str='4.5',
+        target_year: int=2050,
+        **kwargs
     ):
         """
-        From current TC hazard instance, return new hazard set with
-        future events for a given RCP scenario and year based on the
-        parametrized values derived from Table 3 in Knutson et al 2015.
-        https://doi.org/10.1175/JCLI-D-15-0129.1 . The scaling for different
-        years and RCP scenarios is obtained by linear interpolation.
-
-        Note: The parametrized values are derived from the overall changes
-        in statistical ensemble of tracks. Hence, this method should only be
-        applied to sufficiently large tropical cyclone event sets that
-        approximate the reference years 1981 - 2008 used in Knutson et. al.
-
-        The frequency and intensity changes are applied independently from
-        one another. The mean intensity factors can thus slightly deviate
-        from the Knutson value (deviation was found to be less than 1%
-        for default IBTrACS event sets 1980-2020 for each basin).
+        From current TC hazard instance, return new hazard set with future events
+        for a given RCP scenario and year based on the parametrized values derived
+        by Jewson 2021 (https://doi.org/10.1175/JAMC-D-21-0102.1) based on those
+        published by Knutson 2020 (https://doi.org/10.1175/BAMS-D-18-0194.1). The
+        scaling for different years and RCP scenarios is obtained by linear
+        interpolation.
+
+        Note: Only frequency changes are applied as suggested by Jewson 2022
+        (https://doi.org/10.1007/s00477-021-02142-6). Applying only frequency anyway
+        changes mean intensities and most importantly avoids possible inconsistencies
+        (including possible counting) that may arise from the application of both
+        frequency and intensity changes, as the relatioship between these two is non
+        trivial to resolve.
 
         Parameters
         ----------
-        ref_year : int
-            year between 2000 ad 2100. Default: 2050
-        rcp_scenario : int
-            26 for RCP 2.6, 45 for RCP 4.5, 60 for RCP 6.0 and 85 for RCP 8.5.
-            The default is 45.
-
+        percentile: str
+            percentiles of Knutson et al. 2020 estimates, representing the mode
+            uncertainty in future changes in TC activity. These estimates come from
+            a review of state-of-the-art literature and models. For the 'cat05' variable
+            (i.e. frequency of all tropical cyclones) the 5th, 25th, 50th, 75th and 95th
+            percentiles are provided. For 'cat45' and 'intensity', the provided percentiles
+            are the 10th, 25th, 50th, 75th and 90th. Please refer to the mentioned publications
+            for more details.
+            possible percentiles:
+                '5/10' either the 5th or 10th percentile depending on variable (see text above)
+                '25' for the 25th percentile
+                '50' for the 50th percentile
+                '75' for the 75th percentile
+                '90/95' either the 90th or 95th percentile depending on variable  (see text above)
+            Default: '50'
+        scenario : str
+            possible scenarios:
+                '2.6' for RCP 2.6
+                '4.5' for RCP 4.5
+                '6.0' for RCP 6.0
+                '8.5' for RCP 8.5
+        target_year : int
+            future year to be simulated, between 2000 and 2100. Default: 2050.
         Returns
         -------
         haz_cc : climada.hazard.TropCyclone
             Tropical cyclone with frequencies and intensity scaled according
-            to the Knutson criterion for the given year and RCP. Returns
-            a new instance of climada.hazard.TropCyclone, self is not
+            to the Knutson criterion for the given year, RCP and percentile.
+            Returns a new instance of climada.hazard.TropCyclone, self is not
             modified.
         """
-        chg_int_freq = get_knutson_criterion()
-        scale_rcp_year  = calc_scale_knutson(ref_year, rcp_scenario)
-        haz_cc = self._apply_knutson_criterion(chg_int_freq, scale_rcp_year)
-        return haz_cc
+
+        tc_cc = copy.deepcopy(self)
+
+        sel_cat05 = np.isin(tc_cc.category, [0, 1, 2, 3, 4, 5])
+        sel_cat03 = np.isin(tc_cc.category, [0, 1, 2, 3])
+        sel_cat45 = np.isin(tc_cc.category, [4, 5])
+
+        years = np.array([dt.datetime.fromordinal(date).year for date in self.date])
+
+        for basin in np.unique(tc_cc.basin):
+            scale_year_rcp_05, scale_year_rcp_45 = [
+                        get_knutson_scaling_factor(
+                                percentile=percentile,
+                                variable=variable,
+                                basin=basin,
+                                baseline=(np.min(years), np.max(years)),
+                                **kwargs
+                                ).loc[target_year, scenario]
+                                for variable in ['cat05', 'cat45']
+                                ]
+
+            bas_sel = np.array(tc_cc.basin) == basin
+
+            cat_05_freqs_change = scale_year_rcp_05 * np.sum(tc_cc.frequency[sel_cat05 & bas_sel])
+            cat_45_freqs_change = scale_year_rcp_45 * np.sum(tc_cc.frequency[sel_cat45 & bas_sel])
+            cat_03_freqs = np.sum(tc_cc.frequency[sel_cat03 & bas_sel])
+
+            scale_year_rcp_03 = (cat_05_freqs_change-cat_45_freqs_change) / cat_03_freqs
+
+            tc_cc.frequency[sel_cat03 & bas_sel] *= 1 + scale_year_rcp_03/100
+            tc_cc.frequency[sel_cat45 & bas_sel] *= 1 + scale_year_rcp_45/100
+
+            if any(tc_cc.frequency) < 0:
+                raise ValueError(
+                    " The application of the climate scenario leads to "
+                    " negative frequencies. One solution - if appropriate -"
+                    " could be to use a less extreme percentile."
+                    )
+
+        return tc_cc
 
     def set_climate_scenario_knu(self, *args, **kwargs):
         """This function is deprecated, use TropCyclone.apply_climate_scenario_knu instead."""