Explicit arguments in TropCyclone.apply_climate_scenario_knu (#991)

* trop_cyclone.apply_climate_scenario_knu: make yearly_steps argument explicit

* changelog

* readthedocs: explicit configuration file

Author: emanuel-schmid

CHANGELOG.md

@@ -190,6 +190,7 @@ CLIMADA tutorials. [#872](https://github.com/CLIMADA-project/climada_python/pull
 - `Impact.from_hdf5` now calls `str` on `event_name` data that is not strings, and issue a warning then [#894](https://github.com/CLIMADA-project/climada_python/pull/894)
 - `Impact.write_hdf5` now throws an error if `event_name` is does not contain strings exclusively [#894](https://github.com/CLIMADA-project/climada_python/pull/894)
 - Split `climada.hazard.trop_cyclone` module into smaller submodules without affecting module usage [#911](https://github.com/CLIMADA-project/climada_python/pull/911)
+- `yearly_steps` parameter of `TropCyclone.apply_climate_scenario_knu` has been made explicit [#991](https://github.com/CLIMADA-project/climada_python/pull/991)
 
 ### Fixed
 

climada/hazard/trop_cyclone/trop_cyclone.py

@@ -391,7 +391,7 @@ def apply_climate_scenario_knu(
         percentile: str = "50",
         scenario: str = "4.5",
         target_year: int = 2050,
-        **kwargs,
+        yearly_steps: int = 5,
     ):
         """
         From current TC hazard instance, return new hazard set with future events
@@ -437,6 +437,9 @@ def apply_climate_scenario_knu(
 
         target_year : int
             future year to be simulated, between 2000 and 2100. Default: 2050.
+        yearly_steps : int
+            yearly resolution at which projections are provided. Default is 5 years.
+
         Returns
         -------
         haz_cc : climada.hazard.TropCyclone
@@ -465,11 +468,11 @@ def apply_climate_scenario_knu(
         for basin in np.unique(tc_cc.basin):
             scale_year_rcp_05, scale_year_rcp_45 = [
                 get_knutson_scaling_factor(
-                    percentile=percentile,
                     variable=variable,
+                    percentile=percentile,
                     basin=basin,
                     baseline=(np.min(years), np.max(years)),
-                    **kwargs,
+                    yearly_steps=yearly_steps,
                 ).loc[target_year, scenario]
                 for variable in ["cat05", "cat45"]
             ]